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What is a thesis | A Complete Guide with Examples

Table of Contents

A thesis is a comprehensive academic paper based on your original research that presents new findings, arguments, and ideas of your study. It’s typically submitted at the end of your master’s degree or as a capstone of your bachelor’s degree.

However, writing a thesis can be laborious, especially for beginners. From the initial challenge of pinpointing a compelling research topic to organizing and presenting findings, the process is filled with potential pitfalls.

Therefore, to help you, this guide talks about what is a thesis. Additionally, it offers revelations and methodologies to transform it from an overwhelming task to a manageable and rewarding academic milestone.

What is a thesis?

A thesis is an in-depth research study that identifies a particular topic of inquiry and presents a clear argument or perspective about that topic using evidence and logic.

Writing a thesis showcases your ability of critical thinking, gathering evidence, and making a compelling argument. Integral to these competencies is thorough research, which not only fortifies your propositions but also confers credibility to your entire study.

Furthermore, there's another phenomenon you might often confuse with the thesis: the ' working thesis .' However, they aren't similar and shouldn't be used interchangeably.

A working thesis, often referred to as a preliminary or tentative thesis, is an initial version of your thesis statement. It serves as a draft or a starting point that guides your research in its early stages.

As you research more and gather more evidence, your initial thesis (aka working thesis) might change. It's like a starting point that can be adjusted as you learn more. It's normal for your main topic to change a few times before you finalize it.

While a thesis identifies and provides an overarching argument, the key to clearly communicating the central point of that argument lies in writing a strong thesis statement.

What is a thesis statement?

A strong thesis statement (aka thesis sentence) is a concise summary of the main argument or claim of the paper. It serves as a critical anchor in any academic work, succinctly encapsulating the primary argument or main idea of the entire paper.

Typically found within the introductory section, a strong thesis statement acts as a roadmap of your thesis, directing readers through your arguments and findings. By delineating the core focus of your investigation, it offers readers an immediate understanding of the context and the gravity of your study.

Furthermore, an effectively crafted thesis statement can set forth the boundaries of your research, helping readers anticipate the specific areas of inquiry you are addressing.

Different types of thesis statements

A good thesis statement is clear, specific, and arguable. Therefore, it is necessary for you to choose the right type of thesis statement for your academic papers.

Thesis statements can be classified based on their purpose and structure. Here are the primary types of thesis statements:

Argumentative (or Persuasive) thesis statement

Purpose : To convince the reader of a particular stance or point of view by presenting evidence and formulating a compelling argument.

Example : Reducing plastic use in daily life is essential for environmental health.

Analytical thesis statement

Purpose : To break down an idea or issue into its components and evaluate it.

Example : By examining the long-term effects, social implications, and economic impact of climate change, it becomes evident that immediate global action is necessary.

Expository (or Descriptive) thesis statement

Purpose : To explain a topic or subject to the reader.

Example : The Great Depression, spanning the 1930s, was a severe worldwide economic downturn triggered by a stock market crash, bank failures, and reduced consumer spending.

Cause and effect thesis statement

Purpose : To demonstrate a cause and its resulting effect.

Example : Overuse of smartphones can lead to impaired sleep patterns, reduced face-to-face social interactions, and increased levels of anxiety.

Compare and contrast thesis statement

Purpose : To highlight similarities and differences between two subjects.

Example : "While both novels '1984' and 'Brave New World' delve into dystopian futures, they differ in their portrayal of individual freedom, societal control, and the role of technology."

When you write a thesis statement , it's important to ensure clarity and precision, so the reader immediately understands the central focus of your work.

What is the difference between a thesis and a thesis statement?

While both terms are frequently used interchangeably, they have distinct meanings.

A thesis refers to the entire research document, encompassing all its chapters and sections. In contrast, a thesis statement is a brief assertion that encapsulates the central argument of the research.

Here’s an in-depth differentiation table of a thesis and a thesis statement.

Now, to craft a compelling thesis, it's crucial to adhere to a specific structure. Let’s break down these essential components that make up a thesis structure

15 components of a thesis structure

Navigating a thesis can be daunting. However, understanding its structure can make the process more manageable.

Here are the key components or different sections of a thesis structure:

Your thesis begins with the title page. It's not just a formality but the gateway to your research.

Here, you'll prominently display the necessary information about you (the author) and your institutional details.

• Title of your thesis
• Your full name
• Your department
• Your institution and degree program
• Your submission date
• Your Supervisor's name (in some cases)
• Your Department or faculty (in some cases)
• Your University's logo (in some cases)
• Your Student ID (in some cases)

In a concise manner, you'll have to summarize the critical aspects of your research in typically no more than 200-300 words.

This includes the problem statement, methodology, key findings, and conclusions. For many, the abstract will determine if they delve deeper into your work, so ensure it's clear and compelling.

Acknowledgments

Research is rarely a solitary endeavor. In the acknowledgments section, you have the chance to express gratitude to those who've supported your journey.

This might include advisors, peers, institutions, or even personal sources of inspiration and support. It's a personal touch, reflecting the humanity behind the academic rigor.

Table of contents

A roadmap for your readers, the table of contents lists the chapters, sections, and subsections of your thesis.

By providing page numbers, you allow readers to navigate your work easily, jumping to sections that pique their interest.

List of figures and tables

Research often involves data, and presenting this data visually can enhance understanding. This section provides an organized listing of all figures and tables in your thesis.

It's a visual index, ensuring that readers can quickly locate and reference your graphical data.

Introduction

Here's where you introduce your research topic, articulate the research question or objective, and outline the significance of your study.

• Present the research topic : Clearly articulate the central theme or subject of your research.
• Background information : Ground your research topic, providing any necessary context or background information your readers might need to understand the significance of your study.
• Define the scope : Clearly delineate the boundaries of your research, indicating what will and won't be covered.
• Literature review : Introduce any relevant existing research on your topic, situating your work within the broader academic conversation and highlighting where your research fits in.
• State the research Question(s) or objective(s) : Clearly articulate the primary questions or objectives your research aims to address.
• Outline the study's structure : Give a brief overview of how the subsequent sections of your work will unfold, guiding your readers through the journey ahead.

The introduction should captivate your readers, making them eager to delve deeper into your research journey.

Literature review section

Your study correlates with existing research. Therefore, in the literature review section, you'll engage in a dialogue with existing knowledge, highlighting relevant studies, theories, and findings.

It's here that you identify gaps in the current knowledge, positioning your research as a bridge to new insights.

To streamline this process, consider leveraging AI tools. For example, the SciSpace literature review tool enables you to efficiently explore and delve into research papers, simplifying your literature review journey.

Methodology

In the research methodology section, you’ll detail the tools, techniques, and processes you employed to gather and analyze data. This section will inform the readers about how you approached your research questions and ensures the reproducibility of your study.

Here's a breakdown of what it should encompass:

• Research Design : Describe the overall structure and approach of your research. Are you conducting a qualitative study with in-depth interviews? Or is it a quantitative study using statistical analysis? Perhaps it's a mixed-methods approach?
• Data Collection : Detail the methods you used to gather data. This could include surveys, experiments, observations, interviews, archival research, etc. Mention where you sourced your data, the duration of data collection, and any tools or instruments used.
• Sampling : If applicable, explain how you selected participants or data sources for your study. Discuss the size of your sample and the rationale behind choosing it.
• Data Analysis : Describe the techniques and tools you used to process and analyze the data. This could range from statistical tests in quantitative research to thematic analysis in qualitative research.
• Validity and Reliability : Address the steps you took to ensure the validity and reliability of your findings to ensure that your results are both accurate and consistent.
• Ethical Considerations : Highlight any ethical issues related to your research and the measures you took to address them, including — informed consent, confidentiality, and data storage and protection measures.

Moreover, different research questions necessitate different types of methodologies. For instance:

• Experimental methodology : Often used in sciences, this involves a controlled experiment to discern causality.
• Qualitative methodology : Employed when exploring patterns or phenomena without numerical data. Methods can include interviews, focus groups, or content analysis.
• Quantitative methodology : Concerned with measurable data and often involves statistical analysis. Surveys and structured observations are common tools here.
• Mixed methods : As the name implies, this combines both qualitative and quantitative methodologies.

The Methodology section isn’t just about detailing the methods but also justifying why they were chosen. The appropriateness of the methods in addressing your research question can significantly impact the credibility of your findings.

Results (or Findings)

This section presents the outcomes of your research. It's crucial to note that the nature of your results may vary; they could be quantitative, qualitative, or a mix of both.

Quantitative results often present statistical data, showcasing measurable outcomes, and they benefit from tables, graphs, and figures to depict these data points.

Qualitative results , on the other hand, might delve into patterns, themes, or narratives derived from non-numerical data, such as interviews or observations.

Regardless of the nature of your results, clarity is essential. This section is purely about presenting the data without offering interpretations — that comes later in the discussion.

In the discussion section, the raw data transforms into valuable insights.

Start by revisiting your research question and contrast it with the findings. How do your results expand, constrict, or challenge current academic conversations?

Dive into the intricacies of the data, guiding the reader through its implications. Detail potential limitations transparently, signaling your awareness of the research's boundaries. This is where your academic voice should be resonant and confident.

Practical implications (Recommendation) section

Based on the insights derived from your research, this section provides actionable suggestions or proposed solutions.

Whether aimed at industry professionals or the general public, recommendations translate your academic findings into potential real-world actions. They help readers understand the practical implications of your work and how it can be applied to effect change or improvement in a given field.

When crafting recommendations, it's essential to ensure they're feasible and rooted in the evidence provided by your research. They shouldn't merely be aspirational but should offer a clear path forward, grounded in your findings.

The conclusion provides closure to your research narrative.

It's not merely a recap but a synthesis of your main findings and their broader implications. Reconnect with the research questions or hypotheses posited at the beginning, offering clear answers based on your findings.

Reflect on the broader contributions of your study, considering its impact on the academic community and potential real-world applications.

Lastly, the conclusion should leave your readers with a clear understanding of the value and impact of your study.

References (or Bibliography)

Every theory you've expounded upon, every data point you've cited, and every methodological precedent you've followed finds its acknowledgment here.

In references, it's crucial to ensure meticulous consistency in formatting, mirroring the specific guidelines of the chosen citation style .

Proper referencing helps to avoid plagiarism , gives credit to original ideas, and allows readers to explore topics of interest. Moreover, it situates your work within the continuum of academic knowledge.

To properly cite the sources used in the study, you can rely on online citation generator tools  to generate accurate citations!

Here’s more on how you can cite your sources.

Often, the depth of research produces a wealth of material that, while crucial, can make the core content of the thesis cumbersome. The appendix is where you mention extra information that supports your research but isn't central to the main text.

Whether it's raw datasets, detailed procedural methodologies, extended case studies, or any other ancillary material, the appendices ensure that these elements are archived for reference without breaking the main narrative's flow.

For thorough researchers and readers keen on meticulous details, the appendices provide a treasure trove of insights.

Glossary (optional)

In academics, specialized terminologies, and jargon are inevitable. However, not every reader is versed in every term.

The glossary, while optional, is a critical tool for accessibility. It's a bridge ensuring that even readers from outside the discipline can access, understand, and appreciate your work.

By defining complex terms and providing context, you're inviting a wider audience to engage with your research, enhancing its reach and impact.

Remember, while these components provide a structured framework, the essence of your thesis lies in the originality of your ideas, the rigor of your research, and the clarity of your presentation.

As you craft each section, keep your readers in mind, ensuring that your passion and dedication shine through every page.

Thesis examples

To further elucidate the concept of a thesis, here are illustrative examples from various fields:

Example 1 (History): Abolition, Africans, and Abstraction: the Influence of the ‘Noble Savage’ on British and French Antislavery Thought, 1787-1807 by Suchait Kahlon.

Example 2 (Climate Dynamics): Influence of external forcings on abrupt millennial-scale climate changes: a statistical modelling study by Takahito Mitsui · Michel Crucifix

Checklist for your thesis evaluation

Evaluating your thesis ensures that your research meets the standards of academia. Here's an elaborate checklist to guide you through this critical process.

Content and structure

• Is the thesis statement clear, concise, and debatable?
• Does the introduction provide sufficient background and context?
• Is the literature review comprehensive, relevant, and well-organized?
• Does the methodology section clearly describe and justify the research methods?
• Are the results/findings presented clearly and logically?
• Does the discussion interpret the results in light of the research question and existing literature?
• Is the conclusion summarizing the research and suggesting future directions or implications?

Clarity and coherence

• Is the writing clear and free of jargon?
• Are ideas and sections logically connected and flowing?
• Is there a clear narrative or argument throughout the thesis?

Research quality

• Is the research question significant and relevant?
• Are the research methods appropriate for the question?
• Is the sample size (if applicable) adequate?
• Are the data analysis techniques appropriate and correctly applied?
• Are potential biases or limitations addressed?

Originality and significance

• Does the thesis contribute new knowledge or insights to the field?
• Is the research grounded in existing literature while offering fresh perspectives?

Formatting and presentation

• Is the thesis formatted according to institutional guidelines?
• Are figures, tables, and charts clear, labeled, and referenced in the text?
• Is the bibliography or reference list complete and consistently formatted?
• Are appendices relevant and appropriately referenced in the main text?

Grammar and language

• Is the thesis free of grammatical and spelling errors?
• Is the language professional, consistent, and appropriate for an academic audience?
• Are quotations and paraphrased material correctly cited?

Feedback and revision

• Have you sought feedback from peers, advisors, or experts in the field?
• Have you addressed the feedback and made the necessary revisions?

Overall assessment

• Does the thesis as a whole feel cohesive and comprehensive?
• Would the thesis be understandable and valuable to someone in your field?

Ensure to use this checklist to leave no ground for doubt or missed information in your thesis.

After writing your thesis, the next step is to discuss and defend your findings verbally in front of a knowledgeable panel. You’ve to be well prepared as your professors may grade your presentation abilities.

Preparing your thesis defense

A thesis defense, also known as "defending the thesis," is the culmination of a scholar's research journey. It's the final frontier, where you’ll present their findings and face scrutiny from a panel of experts.

Typically, the defense involves a public presentation where you’ll have to outline your study, followed by a question-and-answer session with a committee of experts. This committee assesses the validity, originality, and significance of the research.

The defense serves as a rite of passage for scholars. It's an opportunity to showcase expertise, address criticisms, and refine arguments. A successful defense not only validates the research but also establishes your authority as a researcher in your field.

Here’s how you can effectively prepare for your thesis defense .

Now, having touched upon the process of defending a thesis, it's worth noting that scholarly work can take various forms, depending on academic and regional practices.

One such form, often paralleled with the thesis, is the 'dissertation.' But what differentiates the two?

Dissertation vs. Thesis

Often used interchangeably in casual discourse, they refer to distinct research projects undertaken at different levels of higher education.

To the uninitiated, understanding their meaning might be elusive. So, let's demystify these terms and delve into their core differences.

Here's a table differentiating between the two.

Wrapping up

From understanding the foundational concept of a thesis to navigating its various components, differentiating it from a dissertation, and recognizing the importance of proper citation — this guide covers it all.

As scholars and readers, understanding these nuances not only aids in academic pursuits but also fosters a deeper appreciation for the relentless quest for knowledge that drives academia.

It’s important to remember that every thesis is a testament to curiosity, dedication, and the indomitable spirit of discovery.

Good luck with your thesis writing!

Frequently Asked Questions

A thesis typically ranges between 40-80 pages, but its length can vary based on the research topic, institution guidelines, and level of study.

A PhD thesis usually spans 200-300 pages, though this can vary based on the discipline, complexity of the research, and institutional requirements.

To identify a thesis topic, consider current trends in your field, gaps in existing literature, personal interests, and discussions with advisors or mentors. Additionally, reviewing related journals and conference proceedings can provide insights into potential areas of exploration.

The conceptual framework is often situated in the literature review or theoretical framework section of a thesis. It helps set the stage by providing the context, defining key concepts, and explaining the relationships between variables.

A thesis statement should be concise, clear, and specific. It should state the main argument or point of your research. Start by pinpointing the central question or issue your research addresses, then condense that into a single statement, ensuring it reflects the essence of your paper.

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Definition of thesis

Did you know.

In high school, college, or graduate school, students often have to write a thesis on a topic in their major field of study. In many fields, a final thesis is the biggest challenge involved in getting a master's degree, and the same is true for students studying for a Ph.D. (a Ph.D. thesis is often called a dissertation ). But a thesis may also be an idea; so in the course of the paper the student may put forth several theses (notice the plural form) and attempt to prove them.

Examples of thesis in a Sentence

These examples are programmatically compiled from various online sources to illustrate current usage of the word 'thesis.' Any opinions expressed in the examples do not represent those of Merriam-Webster or its editors. Send us feedback about these examples.

Word History

in sense 3, Middle English, lowering of the voice, from Late Latin & Greek; Late Latin, from Greek, downbeat, more important part of a foot, literally, act of laying down; in other senses, Latin, from Greek, literally, act of laying down, from tithenai to put, lay down — more at do

14th century, in the meaning defined at sense 3a(1)

Dictionary Entries Near thesis

the sins of the fathers are visited upon the children

thesis novel

Cite this Entry

“Thesis.” Merriam-Webster.com Dictionary , Merriam-Webster, https://www.merriam-webster.com/dictionary/thesis. Accessed 16 Apr. 2024.

Kids Definition

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While Sandel argues that pursuing perfection through genetic engineering would decrease our sense of humility, he claims that the sense of solidarity we would lose is also important.

This thesis summarizes several points in Sandel’s argument, but it does not make a claim about how we should understand his argument. A reader who read Sandel’s argument would not also need to read an essay based on this descriptive thesis.  

Broad thesis (arguable, but difficult to support with evidence) 

Michael Sandel’s arguments about genetic engineering do not take into consideration all the relevant issues.

This is an arguable claim because it would be possible to argue against it by saying that Michael Sandel’s arguments do take all of the relevant issues into consideration. But the claim is too broad. Because the thesis does not specify which “issues” it is focused on—or why it matters if they are considered—readers won’t know what the rest of the essay will argue, and the writer won’t know what to focus on. If there is a particular issue that Sandel does not address, then a more specific version of the thesis would include that issue—hand an explanation of why it is important.  

Arguable thesis with analytical claim 

While Sandel argues persuasively that our instinct to “remake” (54) ourselves into something ever more perfect is a problem, his belief that we can always draw a line between what is medically necessary and what makes us simply “better than well” (51) is less convincing.

This is an arguable analytical claim. To argue for this claim, the essay writer will need to show how evidence from the article itself points to this interpretation. It’s also a reasonable scope for a thesis because it can be supported with evidence available in the text and is neither too broad nor too narrow.  

Arguable thesis with normative claim 

Given Sandel’s argument against genetic enhancement, we should not allow parents to decide on using Human Growth Hormone for their children.

This thesis tells us what we should do about a particular issue discussed in Sandel’s article, but it does not tell us how we should understand Sandel’s argument.  

Questions to ask about your thesis 

• Is the thesis truly arguable? Does it speak to a genuine dilemma in the source, or would most readers automatically agree with it?  
• Is the thesis too obvious? Again, would most or all readers agree with it without needing to see your argument?  
• Is the thesis complex enough to require a whole essay's worth of argument?  
• Is the thesis supportable with evidence from the text rather than with generalizations or outside research?  
• Would anyone want to read a paper in which this thesis was developed? That is, can you explain what this paper is adding to our understanding of a problem, question, or topic?
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Home » Thesis – Structure, Example and Writing Guide

Thesis – Structure, Example and Writing Guide

Table of contents.

Definition:

Thesis is a scholarly document that presents a student’s original research and findings on a particular topic or question. It is usually written as a requirement for a graduate degree program and is intended to demonstrate the student’s mastery of the subject matter and their ability to conduct independent research.

History of Thesis

The concept of a thesis can be traced back to ancient Greece, where it was used as a way for students to demonstrate their knowledge of a particular subject. However, the modern form of the thesis as a scholarly document used to earn a degree is a relatively recent development.

The origin of the modern thesis can be traced back to medieval universities in Europe. During this time, students were required to present a “disputation” in which they would defend a particular thesis in front of their peers and faculty members. These disputations served as a way to demonstrate the student’s mastery of the subject matter and were often the final requirement for earning a degree.

In the 17th century, the concept of the thesis was formalized further with the creation of the modern research university. Students were now required to complete a research project and present their findings in a written document, which would serve as the basis for their degree.

The modern thesis as we know it today has evolved over time, with different disciplines and institutions adopting their own standards and formats. However, the basic elements of a thesis – original research, a clear research question, a thorough review of the literature, and a well-argued conclusion – remain the same.

Structure of Thesis

The structure of a thesis may vary slightly depending on the specific requirements of the institution, department, or field of study, but generally, it follows a specific format.

Here’s a breakdown of the structure of a thesis:

This is the first page of the thesis that includes the title of the thesis, the name of the author, the name of the institution, the department, the date, and any other relevant information required by the institution.

This is a brief summary of the thesis that provides an overview of the research question, methodology, findings, and conclusions.

This page provides a list of all the chapters and sections in the thesis and their page numbers.

Introduction

This chapter provides an overview of the research question, the context of the research, and the purpose of the study. The introduction should also outline the methodology and the scope of the research.

Literature Review

This chapter provides a critical analysis of the relevant literature on the research topic. It should demonstrate the gap in the existing knowledge and justify the need for the research.

Methodology

This chapter provides a detailed description of the research methods used to gather and analyze data. It should explain the research design, the sampling method, data collection techniques, and data analysis procedures.

This chapter presents the findings of the research. It should include tables, graphs, and charts to illustrate the results.

This chapter interprets the results and relates them to the research question. It should explain the significance of the findings and their implications for the research topic.

This chapter summarizes the key findings and the main conclusions of the research. It should also provide recommendations for future research.

This section provides a list of all the sources cited in the thesis. The citation style may vary depending on the requirements of the institution or the field of study.

This section includes any additional material that supports the research, such as raw data, survey questionnaires, or other relevant documents.

How to write Thesis

Here are some steps to help you write a thesis:

• Choose a Topic: The first step in writing a thesis is to choose a topic that interests you and is relevant to your field of study. You should also consider the scope of the topic and the availability of resources for research.
• Develop a Research Question: Once you have chosen a topic, you need to develop a research question that you will answer in your thesis. The research question should be specific, clear, and feasible.
• Conduct a Literature Review: Before you start your research, you need to conduct a literature review to identify the existing knowledge and gaps in the field. This will help you refine your research question and develop a research methodology.
• Develop a Research Methodology: Once you have refined your research question, you need to develop a research methodology that includes the research design, data collection methods, and data analysis procedures.
• Collect and Analyze Data: After developing your research methodology, you need to collect and analyze data. This may involve conducting surveys, interviews, experiments, or analyzing existing data.
• Write the Thesis: Once you have analyzed the data, you need to write the thesis. The thesis should follow a specific structure that includes an introduction, literature review, methodology, results, discussion, conclusion, and references.
• Edit and Proofread: After completing the thesis, you need to edit and proofread it carefully. You should also have someone else review it to ensure that it is clear, concise, and free of errors.
• Submit the Thesis: Finally, you need to submit the thesis to your academic advisor or committee for review and evaluation.

Example of Thesis

Example of Thesis template for Students:

Title of Thesis

Table of Contents:

Chapter 1: Introduction

Chapter 2: Literature Review

Chapter 3: Research Methodology

Chapter 4: Results

Chapter 5: Discussion

Chapter 6: Conclusion

References:

Appendices:

Note: That’s just a basic template, but it should give you an idea of the structure and content that a typical thesis might include. Be sure to consult with your department or supervisor for any specific formatting requirements they may have. Good luck with your thesis!

Application of Thesis

Thesis is an important academic document that serves several purposes. Here are some of the applications of thesis:

• Academic Requirement: A thesis is a requirement for many academic programs, especially at the graduate level. It is an essential component of the evaluation process and demonstrates the student’s ability to conduct original research and contribute to the knowledge in their field.
• Career Advancement: A thesis can also help in career advancement. Employers often value candidates who have completed a thesis as it demonstrates their research skills, critical thinking abilities, and their dedication to their field of study.
• Publication : A thesis can serve as a basis for future publications in academic journals, books, or conference proceedings. It provides the researcher with an opportunity to present their research to a wider audience and contribute to the body of knowledge in their field.
• Personal Development: Writing a thesis is a challenging task that requires time, dedication, and perseverance. It provides the student with an opportunity to develop critical thinking, research, and writing skills that are essential for their personal and professional development.
• Impact on Society: The findings of a thesis can have an impact on society by addressing important issues, providing insights into complex problems, and contributing to the development of policies and practices.

Purpose of Thesis

The purpose of a thesis is to present original research findings in a clear and organized manner. It is a formal document that demonstrates a student’s ability to conduct independent research and contribute to the knowledge in their field of study. The primary purposes of a thesis are:

• To Contribute to Knowledge: The main purpose of a thesis is to contribute to the knowledge in a particular field of study. By conducting original research and presenting their findings, the student adds new insights and perspectives to the existing body of knowledge.
• To Demonstrate Research Skills: A thesis is an opportunity for the student to demonstrate their research skills. This includes the ability to formulate a research question, design a research methodology, collect and analyze data, and draw conclusions based on their findings.
• To Develop Critical Thinking: Writing a thesis requires critical thinking and analysis. The student must evaluate existing literature and identify gaps in the field, as well as develop and defend their own ideas.
• To Provide Evidence of Competence : A thesis provides evidence of the student’s competence in their field of study. It demonstrates their ability to apply theoretical concepts to real-world problems, and their ability to communicate their ideas effectively.
• To Facilitate Career Advancement : Completing a thesis can help the student advance their career by demonstrating their research skills and dedication to their field of study. It can also provide a basis for future publications, presentations, or research projects.

When to Write Thesis

The timing for writing a thesis depends on the specific requirements of the academic program or institution. In most cases, the opportunity to write a thesis is typically offered at the graduate level, but there may be exceptions.

Generally, students should plan to write their thesis during the final year of their graduate program. This allows sufficient time for conducting research, analyzing data, and writing the thesis. It is important to start planning the thesis early and to identify a research topic and research advisor as soon as possible.

In some cases, students may be able to write a thesis as part of an undergraduate program or as an independent research project outside of an academic program. In such cases, it is important to consult with faculty advisors or mentors to ensure that the research is appropriately designed and executed.

It is important to note that the process of writing a thesis can be time-consuming and requires a significant amount of effort and dedication. It is important to plan accordingly and to allocate sufficient time for conducting research, analyzing data, and writing the thesis.

Characteristics of Thesis

The characteristics of a thesis vary depending on the specific academic program or institution. However, some general characteristics of a thesis include:

• Originality : A thesis should present original research findings or insights. It should demonstrate the student’s ability to conduct independent research and contribute to the knowledge in their field of study.
• Clarity : A thesis should be clear and concise. It should present the research question, methodology, findings, and conclusions in a logical and organized manner. It should also be well-written, with proper grammar, spelling, and punctuation.
• Research-Based: A thesis should be based on rigorous research, which involves collecting and analyzing data from various sources. The research should be well-designed, with appropriate research methods and techniques.
• Evidence-Based : A thesis should be based on evidence, which means that all claims made in the thesis should be supported by data or literature. The evidence should be properly cited using appropriate citation styles.
• Critical Thinking: A thesis should demonstrate the student’s ability to critically analyze and evaluate information. It should present the student’s own ideas and arguments, and engage with existing literature in the field.
• Academic Style : A thesis should adhere to the conventions of academic writing. It should be well-structured, with clear headings and subheadings, and should use appropriate academic language.

Advantages of Thesis

There are several advantages to writing a thesis, including:

• Development of Research Skills: Writing a thesis requires extensive research and analytical skills. It helps to develop the student’s research skills, including the ability to formulate research questions, design and execute research methodologies, collect and analyze data, and draw conclusions based on their findings.
• Contribution to Knowledge: Writing a thesis provides an opportunity for the student to contribute to the knowledge in their field of study. By conducting original research, they can add new insights and perspectives to the existing body of knowledge.
• Preparation for Future Research: Completing a thesis prepares the student for future research projects. It provides them with the necessary skills to design and execute research methodologies, analyze data, and draw conclusions based on their findings.
• Career Advancement: Writing a thesis can help to advance the student’s career. It demonstrates their research skills and dedication to their field of study, and provides a basis for future publications, presentations, or research projects.
• Personal Growth: Completing a thesis can be a challenging and rewarding experience. It requires dedication, hard work, and perseverance. It can help the student to develop self-confidence, independence, and a sense of accomplishment.

Limitations of Thesis

There are also some limitations to writing a thesis, including:

• Time and Resources: Writing a thesis requires a significant amount of time and resources. It can be a time-consuming and expensive process, as it may involve conducting original research, analyzing data, and producing a lengthy document.
• Narrow Focus: A thesis is typically focused on a specific research question or topic, which may limit the student’s exposure to other areas within their field of study.
• Limited Audience: A thesis is usually only read by a small number of people, such as the student’s thesis advisor and committee members. This limits the potential impact of the research findings.
• Lack of Real-World Application : Some thesis topics may be highly theoretical or academic in nature, which may limit their practical application in the real world.
• Pressure and Stress : Writing a thesis can be a stressful and pressure-filled experience, as it may involve meeting strict deadlines, conducting original research, and producing a high-quality document.
• Potential for Isolation: Writing a thesis can be a solitary experience, as the student may spend a significant amount of time working independently on their research and writing.

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The Quintessence of Basic and Clinical Research and Scientific Publishing pp 769–781 Cite as

Writing a Postgraduate or Doctoral Thesis: A Step-by-Step Approach

• Usha Y. Nayak 4 ,
• Praveen Hoogar 5 ,
• Srinivas Mutalik 4 &
• N. Udupa 6  
• First Online: 01 October 2023

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1 Citations

A key characteristic looked after by postgraduate or doctoral students is how they communicate and defend their knowledge. Many candidates believe that there is insufficient instruction on constructing strong arguments. The thesis writing procedure must be meticulously followed to achieve outstanding results. It should be well organized, simple to read, and provide detailed explanations of the core research concepts. Each section in a thesis should be carefully written to make sure that it transitions logically from one to the next in a smooth way and is free of any unclear, cluttered, or redundant elements that make it difficult for the reader to understand what is being tried to convey. In this regard, students must acquire the information and skills to successfully create a strong and effective thesis. A step-by-step description of the thesis/dissertation writing process is provided in this chapter.

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Nayak, U.Y., Hoogar, P., Mutalik, S., Udupa, N. (2023). Writing a Postgraduate or Doctoral Thesis: A Step-by-Step Approach. In: Jagadeesh, G., Balakumar, P., Senatore, F. (eds) The Quintessence of Basic and Clinical Research and Scientific Publishing. Springer, Singapore. https://doi.org/10.1007/978-981-99-1284-1_48

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How to Write a Science Thesis/Dissertation

A thesis/dissertation is a long, high-level research paper written as the culmination of your academic course. Most university programs require that graduate and postgraduate students demonstrate their ability to perform original research at the thesis/dissertation level as a graduation requirement.

Not all theses/dissertations are structured the same way. In this article, we’ll specifically look at how to structure a thesis/dissertation in the sciences and examine what belongs in each section. Before you begin writing, it is essential to have a good understanding of how to structure your science thesis/dissertation and what elements you must include in it.

How are science theses/dissertations structured?

There isn’t a universal format for a science thesis/dissertation. Each university/institution has its own rules, and these rules can vary further by department and advisor. For this reason, you must start writing/drafting your thesis/dissertation by checking the rules and requirements of your university/institution.

Some universities mandate a minimum word count for a thesis/dissertation, while others provide a maximum. The number of words you are expected to write will also vary depending on the program/course you are a part of. A Master’s level thesis/dissertation can range, for example, from 15,000 to 45,000 words, while a PhD thesis/dissertation can be around 80,000 words.

While your university/institution may have its own specific requirements or guidelines, this article provides a general overview of how a typical thesis/dissertation in the sciences should be structured. For easier understanding, let’s break it up into two parts:

• Thesis body
• Supplemental information

The thesis body of your thesis/dissertation includes:

• Acknowledgements

Table of contents

Introduction/literature review, materials/methodology, discussion/conclusion, figure and tables, list of abbreviations.

Your thesis will conclude with the supplemental information section, which comprises:

Reference list

Your thesis may or may not include each and every one of these sections. Now, let’s examine the parts of a thesis/dissertation in greater detail.

The parts of a science thesis/dissertation: Getting started

Let’s begin by reviewing the sections of the thesis body, from the title page to the glossary. This part of your thesis/dissertation should ideally be written last, even though it comes at the beginning. That is because it is the easiest to put it togethe r once you have written the rest of your thesis/dissertation.

Your thesis/dissertation should have a clear title that sums up the content. In addition, the title page should include your name, the degree of your thesis/dissertation, your department, your advisor, and the month/year of submission. Your university/institution likely has its own format for what should be included in the title page, so make sure to check the relevant guidelines.

Acknowledgments

This section gives you the opportunity to say thanks to anyone who gave you support while you worked on your thesis/dissertation. Many people use this section to give credit to their advisor, editor, or even their parents. If you received any funding for your research or technical assistance, make sure to mention it here.

Your abstract should be a brief summary (generally around 300 words) of your thesis/dissertation. You can think of your abstract as a distillation of your thesis/dissertation as a whole. You need to summarize the scope and objectives, methods, and findings in this section.

 The table of contents is a directory of the various parts of your thesis/dissertation. It should include the headings and subheadings of each section along with the page numbers where those sections can be found.

 Think of this section as the table of contents for figures and tables in your thesis/dissertation. The titles of each figure/table and the page number where it can be found should be in this list.

This list is intended to identify specialized abbreviations used throughout your thesis/dissertation. This can include the names of organizations (WHO, CDC), acronyms (PFC), and so on. For a science thesis/dissertation, it is preferable also to include a note regarding any abbreviations for units of measurement and standard notations for chemical elements, formulae, and chemical abbreviations used.

In this section, you would define any terminology that your target audience may be unfamiliar with.

The parts of a science thesis/dissertation: Presenting your data

Following the glossary, the thesis body of a science thesis/dissertation begins with the introduction. The introduction section of a science thesis/dissertation often also includes the literature review. This is unlike most social science or humanities theses/dissertations, where the literature review commonly forms a separate chapter. The introduction section should begin by clearly stating the background and context for your research study, followed by your thesis question, objectives, hypothesis , and thesis statement . An example might be: 

“The connection between nicotine consumption and insulin resistance has long been established. However, there is no substantial body of research on how long insulin resistance is maintained after people quit smoking. In this study, we aim to measure levels of insulin resistance in otherwise healthy subjects following a total cessation of nicotine consumption. We hypothesize that insulin resistance will begin to decline rapidly within six months.”

 The introduction should be immediately followed by a review of earlier literature written on the thesis topic. In this section, you should also clearly identify where the literature connects to your study and how your research study fills a gap or bolsters previous studies. Fit your study within the puzzle of previous work and demonstrate the importance of your research.

In the methodology section of your thesis/dissertation, you must explain what you did and how you did it. If you used materials (for example, bacteria), make sure you clearly list each one. Live materials should be listed, including the specific strain and genus. You must explain your techniques, materials, and methods such that another researcher can replicate exactly what you have done.

In the results section, you will explain what happened. What were your findings? This section should be heavy on data and light on analysis. Usually, in-depth analysis and interpretation of your results will be covered in the discussion section of your thesis/dissertation. While you should present your results in full, any supplementary data that you don’t have room for can be included in an appendix. As a note, this section is often written in the past tense. While other portions of your thesis/dissertation may use past and present interchangeably depending on the topic at hand, the results section of a scientific paper focuses on what has already happened (in an experiment), which is why it is written this way.

In this part of your thesis/dissertation, you will discuss what your findings mean. Did they align with your hypothesis? If so, how? If not, what was different? If there were any exceptions, errors, or total lack of correlation found, do not try to hide it. Clearly discuss what it might mean, or if you aren’t sure, don’t be afraid to say so. In this section, you can also highlight potential practical applications for your research study, limitations of your study, directions for future studies, and once again highlight the importance of your study in the field. This section usually concludes with an overall summarization of whether your results support your hypothesis or not. For example:

“Our study found that 500 of our 600 subjects continued to exhibit high levels of insulin resistance three years or more after stopping nicotine use. This does not support our hypothesis that insulin resistance would begin to drop around six months after subjects stopped nicotine use. Further research is warranted into the mechanisms by which past nicotine use alters insulin resistance levels in former smokers.”

The reference list is an alphabetical or numerical list of sources you’ve used while researching and writing your thesis. The formatting of your reference list will be dependent on your university guidelines. Useful tools like citation generators can help you correctly format your references. Reference managers like EndNote or Mendeley are also helpful for compiling this list. Furthermore, a professional editor or proofreading service can ensure that each reference is correctly formatted.

This section can be very useful if you want to include materials that are relevant to the topic of your thesis/dissertation but that you were unable to include in the main text. Tables, large bodies of text, illustrations, forms used to collect data or perform studies, and other such materials can all be included in an appendix.

Critical steps for planning, drafting, and structuring a science thesis/dissertation

Writing your thesis/dissertation is a daunting and lengthy task. Here are some helpful tips to keep in mind when drafting your science thesis/dissertation:

• Choose a thesis topic that is of professional interest to you. You are going to spend a lot of time thinking, reading, and writing about your thesis topic. Many aspiring young researchers end up working in a field related to their thesis/dissertation . If you start researching or writing a proposal and then decide you aren’t into the topic, don’t be afraid to change directions!
• Plan your thesis timelines carefully. Is your topic realistic given the time and material constraints you have? Do you need to apply for external funding for your research study? Will that take additional time? Write a schedule and revisit/revise it often throughout your thesis/dissertation process.
• Don’t wait until the last minute to start writing! A thesis/dissertation isn’t like an undergraduate paper where you spend some time researching and then some time writing it. You will need to write your thesis/dissertation as you continue your research study. Write as you work in the lab. Write as you learn things and then revise. Ideally, by the time you have finished your actual research study, you will already have a substantive draft.
• Start writing the methodology section first. This is often the easiest because it is straightforward and you have already done quite a lot of the work while preparing your research study. The order in which you write your thesis/dissertation doesn’t matter too much—if you find yourself jumping between sections, that is perfectly normal.
• Keep a detailed list of your references using a reference manager or similar system, with tags so that you can easily identify the source of your information.

Final tips for writing and structuring a science thesis/dissertation

Writing a thesis/dissertation is a rewarding process. As a final tip for getting through this process successfully, don’t forget to leave sufficient time for editing and proofreading. Your thesis/dissertation will go through many drafts and revisions before it reaches its final form.

Engaging the services of a professional can go a long way in helping you produce a professional and high-quality document worthy of your research. In addition, there are many helpful tools like AI grammar checker tools available online for students and young researchers.

Check out our site for more tips on how to write a good thesis/dissertation , where to find the best thesis editing services , and more about thesis editing and proofreading services .

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Review Checklist

Use this checklist to ensure that your science thesis/dissertation isn’t missing any important structural components.

Title page: Does your thesis/dissertation have a title page with your title, name, department, advisor’s name, and other important information?

Acknowledgements: Did you give credit to your funders, research colleagues, and anyone else who helped you?

Abstract: Does your thesis/dissertation include a brief summary?

Table of contents: Does your table of contents include headings, subheadings, and page numbers?

Figure and tables: Is there a complete list of figures and tables that are in your thesis/dissertation?

List of abbreviations: Are all of the abbreviations used in your thesis/dissertation listed here?

Glossary: Did you clearly define any specialized terminology used in your thesis/dissertation?

Introduction/Literature review: Did you justify your research study, state your objectives, and your hypothesis? Did you review the previous relevant literature in your field and explain how your thesis/dissertation fits in?

Materials/Methodology: Could another scientist replicate what you did by reading this section?

Results: Did you include all of the data from your experiments/research study?

Discussion/Conclusion: Did you clearly explain what your results mean and whether your hypothesis was correct or not?

Reference list: Are your references properly formatted and listed alphabetically or numerically?

Bibliography and Appendices: Did you include any additional relevant data, figures, or text that didn’t fit into the main section of your thesis/dissertation?

How long is a typical science thesis/dissertation? +

A typical Master’s thesis/dissertation ranges from 15,000-45,000 words, while a Ph.D. thesis/dissertation can be as much as 80,000 words.

How do I start my thesis/dissertation? +

You don’t have to start with the introduction when you begin writing. You can start with the methodology section or any other section you prefer and revise it later.

How do I structure a science thesis/dissertation? +

The main section of a science thesis/dissertation includes an introduction/literature review, materials/methodology section, results, discussion/conclusion section, and a references list.

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Definition of thesis noun from the Oxford Advanced Learner's Dictionary

• Students must submit a thesis on an agreed subject within four years.
• He presented this thesis for his PhD.
• a thesis for a master's degree
• He's doing a doctoral thesis on the early works of Shostakovich.
• Many departments require their students to do a thesis defense.
• She completed an MSc by thesis.
• her thesis adviser at MIT
• in a/​the thesis
• thesis about

Questions about grammar and vocabulary?

Find the answers with Practical English Usage online, your indispensable guide to problems in English.

• The basic thesis of the book is fairly simple.
• These latest findings support the thesis that sexuality is determined by nature rather than choice.
• formulate/​advance a theory/​hypothesis
• build/​construct/​create/​develop a simple/​theoretical/​mathematical model
• develop/​establish/​provide/​use a theoretical/​conceptual framework
• advance/​argue/​develop the thesis that…
• explore an idea/​a concept/​a hypothesis
• make a prediction/​an inference
• base a prediction/​your calculations on something
• investigate/​evaluate/​accept/​challenge/​reject a theory/​hypothesis/​model
• design an experiment/​a questionnaire/​a study/​a test
• do research/​an experiment/​an analysis
• make observations/​measurements/​calculations
• carry out/​conduct/​perform an experiment/​a test/​a longitudinal study/​observations/​clinical trials
• run an experiment/​a simulation/​clinical trials
• repeat an experiment/​a test/​an analysis
• replicate a study/​the results/​the findings
• observe/​study/​examine/​investigate/​assess a pattern/​a process/​a behaviour
• fund/​support the research/​project/​study
• seek/​provide/​get/​secure funding for research
• collect/​gather/​extract data/​information
• yield data/​evidence/​similar findings/​the same results
• analyse/​examine the data/​soil samples/​a specimen
• consider/​compare/​interpret the results/​findings
• fit the data/​model
• confirm/​support/​verify a prediction/​a hypothesis/​the results/​the findings
• prove a conjecture/​hypothesis/​theorem
• draw/​make/​reach the same conclusions
• read/​review the records/​literature
• describe/​report an experiment/​a study
• present/​publish/​summarize the results/​findings
• present/​publish/​read/​review/​cite a paper in a scientific journal
• The results of the experiment support his central thesis.
• Most people rejected this thesis at the time because it presumed evolution rather than creation.
• fundamental

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• Dissertation

How to Write a Thesis or Dissertation Introduction

Published on September 7, 2022 by Tegan George and Shona McCombes. Revised on November 21, 2023.

The introduction is the first section of your thesis or dissertation , appearing right after the table of contents . Your introduction draws your reader in, setting the stage for your research with a clear focus, purpose, and direction on a relevant topic .

Your introduction should include:

• Your topic, in context: what does your reader need to know to understand your thesis dissertation?
• Your focus and scope: what specific aspect of the topic will you address?
• The relevance of your research: how does your work fit into existing studies on your topic?
• Your questions and objectives: what does your research aim to find out, and how?
• An overview of your structure: what does each section contribute to the overall aim?

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Table of contents

How to start your introduction, topic and context, focus and scope, relevance and importance, questions and objectives, overview of the structure, thesis introduction example, introduction checklist, other interesting articles, frequently asked questions about introductions.

Although your introduction kicks off your dissertation, it doesn’t have to be the first thing you write — in fact, it’s often one of the very last parts to be completed (just before your abstract ).

It’s a good idea to write a rough draft of your introduction as you begin your research, to help guide you. If you wrote a research proposal , consider using this as a template, as it contains many of the same elements. However, be sure to revise your introduction throughout the writing process, making sure it matches the content of your ensuing sections.

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Begin by introducing your dissertation topic and giving any necessary background information. It’s important to contextualize your research and generate interest. Aim to show why your topic is timely or important. You may want to mention a relevant news item, academic debate, or practical problem.

After a brief introduction to your general area of interest, narrow your focus and define the scope of your research.

You can narrow this down in many ways, such as by:

• Geographical area
• Time period
• Demographics or communities
• Themes or aspects of the topic

It’s essential to share your motivation for doing this research, as well as how it relates to existing work on your topic. Further, you should also mention what new insights you expect it will contribute.

Start by giving a brief overview of the current state of research. You should definitely cite the most relevant literature, but remember that you will conduct a more in-depth survey of relevant sources in the literature review section, so there’s no need to go too in-depth in the introduction.

Depending on your field, the importance of your research might focus on its practical application (e.g., in policy or management) or on advancing scholarly understanding of the topic (e.g., by developing theories or adding new empirical data). In many cases, it will do both.

Ultimately, your introduction should explain how your thesis or dissertation:

• Helps solve a practical or theoretical problem
• Addresses a gap in the literature
• Builds on existing research
• Proposes a new understanding of your topic

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Perhaps the most important part of your introduction is your questions and objectives, as it sets up the expectations for the rest of your thesis or dissertation. How you formulate your research questions and research objectives will depend on your discipline, topic, and focus, but you should always clearly state the central aim of your research.

If your research aims to test hypotheses , you can formulate them here. Your introduction is also a good place for a conceptual framework that suggests relationships between variables .

• Conduct surveys to collect data on students’ levels of knowledge, understanding, and positive/negative perceptions of government policy.
• Determine whether attitudes to climate policy are associated with variables such as age, gender, region, and social class.
• Conduct interviews to gain qualitative insights into students’ perspectives and actions in relation to climate policy.

To help guide your reader, end your introduction with an outline  of the structure of the thesis or dissertation to follow. Share a brief summary of each chapter, clearly showing how each contributes to your central aims. However, be careful to keep this overview concise: 1-2 sentences should be enough.

I. Introduction

Human language consists of a set of vowels and consonants which are combined to form words. During the speech production process, thoughts are converted into spoken utterances to convey a message. The appropriate words and their meanings are selected in the mental lexicon (Dell & Burger, 1997). This pre-verbal message is then grammatically coded, during which a syntactic representation of the utterance is built.

Speech, language, and voice disorders affect the vocal cords, nerves, muscles, and brain structures, which result in a distorted language reception or speech production (Sataloff & Hawkshaw, 2014). The symptoms vary from adding superfluous words and taking pauses to hoarseness of the voice, depending on the type of disorder (Dodd, 2005). However, distortions of the speech may also occur as a result of a disease that seems unrelated to speech, such as multiple sclerosis or chronic obstructive pulmonary disease.

This study aims to determine which acoustic parameters are suitable for the automatic detection of exacerbations in patients suffering from chronic obstructive pulmonary disease (COPD) by investigating which aspects of speech differ between COPD patients and healthy speakers and which aspects differ between COPD patients in exacerbation and stable COPD patients.

Checklist: Introduction

I have introduced my research topic in an engaging way.

I have provided necessary context to help the reader understand my topic.

I have clearly specified the focus of my research.

I have shown the relevance and importance of the dissertation topic .

I have clearly stated the problem or question that my research addresses.

I have outlined the specific objectives of the research .

I have provided an overview of the dissertation’s structure .

You've written a strong introduction for your thesis or dissertation. Use the other checklists to continue improving your dissertation.

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The introduction of a research paper includes several key elements:

• A hook to catch the reader’s interest
• Relevant background on the topic
• Details of your research problem

and your problem statement

• A thesis statement or research question
• Sometimes an overview of the paper

Don’t feel that you have to write the introduction first. The introduction is often one of the last parts of the research paper you’ll write, along with the conclusion.

This is because it can be easier to introduce your paper once you’ve already written the body ; you may not have the clearest idea of your arguments until you’ve written them, and things can change during the writing process .

Research objectives describe what you intend your research project to accomplish.

They summarize the approach and purpose of the project and help to focus your research.

Your objectives should appear in the introduction of your research paper , at the end of your problem statement .

Scope of research is determined at the beginning of your research process , prior to the data collection stage. Sometimes called “scope of study,” your scope delineates what will and will not be covered in your project. It helps you focus your work and your time, ensuring that you’ll be able to achieve your goals and outcomes.

Defining a scope can be very useful in any research project, from a research proposal to a thesis or dissertation . A scope is needed for all types of research: quantitative , qualitative , and mixed methods .

To define your scope of research, consider the following:

• Budget constraints or any specifics of grant funding
• Your proposed timeline and duration
• Specifics about your population of study, your proposed sample size , and the research methodology you’ll pursue
• Any inclusion and exclusion criteria
• Any anticipated control , extraneous , or confounding variables that could bias your research if not accounted for properly.

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What Is A Research (Scientific) Hypothesis? A plain-language explainer + examples

By:  Derek Jansen (MBA)  | Reviewed By: Dr Eunice Rautenbach | June 2020

If you’re new to the world of research, or it’s your first time writing a dissertation or thesis, you’re probably noticing that the words “research hypothesis” and “scientific hypothesis” are used quite a bit, and you’re wondering what they mean in a research context .

“Hypothesis” is one of those words that people use loosely, thinking they understand what it means. However, it has a very specific meaning within academic research. So, it’s important to understand the exact meaning before you start hypothesizing. 

Research Hypothesis 101

• What is a hypothesis ?
• What is a research hypothesis (scientific hypothesis)?
• Requirements for a research hypothesis
• Definition of a research hypothesis
• The null hypothesis

What is a hypothesis?

Let’s start with the general definition of a hypothesis (not a research hypothesis or scientific hypothesis), according to the Cambridge Dictionary:

Hypothesis: an idea or explanation for something that is based on known facts but has not yet been proved.

In other words, it’s a statement that provides an explanation for why or how something works, based on facts (or some reasonable assumptions), but that has not yet been specifically tested . For example, a hypothesis might look something like this:

Hypothesis: sleep impacts academic performance.

This statement predicts that academic performance will be influenced by the amount and/or quality of sleep a student engages in – sounds reasonable, right? It’s based on reasonable assumptions , underpinned by what we currently know about sleep and health (from the existing literature). So, loosely speaking, we could call it a hypothesis, at least by the dictionary definition.

But that’s not good enough…

Unfortunately, that’s not quite sophisticated enough to describe a research hypothesis (also sometimes called a scientific hypothesis), and it wouldn’t be acceptable in a dissertation, thesis or research paper . In the world of academic research, a statement needs a few more criteria to constitute a true research hypothesis .

What is a research hypothesis?

A research hypothesis (also called a scientific hypothesis) is a statement about the expected outcome of a study (for example, a dissertation or thesis). To constitute a quality hypothesis, the statement needs to have three attributes – specificity , clarity and testability .

Let’s take a look at these more closely.

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Hypothesis Essential #1: Specificity & Clarity

A good research hypothesis needs to be extremely clear and articulate about both what’ s being assessed (who or what variables are involved ) and the expected outcome (for example, a difference between groups, a relationship between variables, etc.).

Let’s stick with our sleepy students example and look at how this statement could be more specific and clear.

Hypothesis: Students who sleep at least 8 hours per night will, on average, achieve higher grades in standardised tests than students who sleep less than 8 hours a night.

As you can see, the statement is very specific as it identifies the variables involved (sleep hours and test grades), the parties involved (two groups of students), as well as the predicted relationship type (a positive relationship). There’s no ambiguity or uncertainty about who or what is involved in the statement, and the expected outcome is clear.

Contrast that to the original hypothesis we looked at – “Sleep impacts academic performance” – and you can see the difference. “Sleep” and “academic performance” are both comparatively vague , and there’s no indication of what the expected relationship direction is (more sleep or less sleep). As you can see, specificity and clarity are key.

Hypothesis Essential #2: Testability (Provability)

A statement must be testable to qualify as a research hypothesis. In other words, there needs to be a way to prove (or disprove) the statement. If it’s not testable, it’s not a hypothesis – simple as that.

For example, consider the hypothesis we mentioned earlier:

Hypothesis: Students who sleep at least 8 hours per night will, on average, achieve higher grades in standardised tests than students who sleep less than 8 hours a night.  

We could test this statement by undertaking a quantitative study involving two groups of students, one that gets 8 or more hours of sleep per night for a fixed period, and one that gets less. We could then compare the standardised test results for both groups to see if there’s a statistically significant difference. 

Again, if you compare this to the original hypothesis we looked at – “Sleep impacts academic performance” – you can see that it would be quite difficult to test that statement, primarily because it isn’t specific enough. How much sleep? By who? What type of academic performance?

So, remember the mantra – if you can’t test it, it’s not a hypothesis 🙂

Defining A Research Hypothesis

You’re still with us? Great! Let’s recap and pin down a clear definition of a hypothesis.

A research hypothesis (or scientific hypothesis) is a statement about an expected relationship between variables, or explanation of an occurrence, that is clear, specific and testable.

So, when you write up hypotheses for your dissertation or thesis, make sure that they meet all these criteria. If you do, you’ll not only have rock-solid hypotheses but you’ll also ensure a clear focus for your entire research project.

What about the null hypothesis?

You may have also heard the terms null hypothesis , alternative hypothesis, or H-zero thrown around. At a simple level, the null hypothesis is the counter-proposal to the original hypothesis.

For example, if the hypothesis predicts that there is a relationship between two variables (for example, sleep and academic performance), the null hypothesis would predict that there is no relationship between those variables.

At a more technical level, the null hypothesis proposes that no statistical significance exists in a set of given observations and that any differences are due to chance alone.

And there you have it – hypotheses in a nutshell. 

If you have any questions, be sure to leave a comment below and we’ll do our best to help you. If you need hands-on help developing and testing your hypotheses, consider our private coaching service , where we hold your hand through the research journey.

Psst… there’s more (for free)

This post is part of our dissertation mini-course, which covers everything you need to get started with your dissertation, thesis or research project. 

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16 Comments

Very useful information. I benefit more from getting more information in this regard.

Very great insight,educative and informative. Please give meet deep critics on many research data of public international Law like human rights, environment, natural resources, law of the sea etc

In a book I read a distinction is made between null, research, and alternative hypothesis. As far as I understand, alternative and research hypotheses are the same. Can you please elaborate? Best Afshin

This is a self explanatory, easy going site. I will recommend this to my friends and colleagues.

Very good definition. How can I cite your definition in my thesis? Thank you. Is nul hypothesis compulsory in a research?

It’s a counter-proposal to be proven as a rejection

Please what is the difference between alternate hypothesis and research hypothesis?

It is a very good explanation. However, it limits hypotheses to statistically tasteable ideas. What about for qualitative researches or other researches that involve quantitative data that don’t need statistical tests?

In qualitative research, one typically uses propositions, not hypotheses.

could you please elaborate it more

I’ve benefited greatly from these notes, thank you.

This is very helpful

well articulated ideas are presented here, thank you for being reliable sources of information

Excellent. Thanks for being clear and sound about the research methodology and hypothesis (quantitative research)

I have only a simple question regarding the null hypothesis. – Is the null hypothesis (Ho) known as the reversible hypothesis of the alternative hypothesis (H1? – How to test it in academic research?

this is very important note help me much more

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• Aims and Objectives – A Guide for Academic Writing
• Doing a PhD

One of the most important aspects of a thesis, dissertation or research paper is the correct formulation of the aims and objectives. This is because your aims and objectives will establish the scope, depth and direction that your research will ultimately take. An effective set of aims and objectives will give your research focus and your reader clarity, with your aims indicating what is to be achieved, and your objectives indicating how it will be achieved.

Introduction

There is no getting away from the importance of the aims and objectives in determining the success of your research project. Unfortunately, however, it is an aspect that many students struggle with, and ultimately end up doing poorly. Given their importance, if you suspect that there is even the smallest possibility that you belong to this group of students, we strongly recommend you read this page in full.

This page describes what research aims and objectives are, how they differ from each other, how to write them correctly, and the common mistakes students make and how to avoid them. An example of a good aim and objectives from a past thesis has also been deconstructed to help your understanding.

What Are Aims and Objectives?

Research aims.

A research aim describes the main goal or the overarching purpose of your research project.

In doing so, it acts as a focal point for your research and provides your readers with clarity as to what your study is all about. Because of this, research aims are almost always located within its own subsection under the introduction section of a research document, regardless of whether it’s a thesis , a dissertation, or a research paper .

A research aim is usually formulated as a broad statement of the main goal of the research and can range in length from a single sentence to a short paragraph. Although the exact format may vary according to preference, they should all describe why your research is needed (i.e. the context), what it sets out to accomplish (the actual aim) and, briefly, how it intends to accomplish it (overview of your objectives).

To give an example, we have extracted the following research aim from a real PhD thesis:

Example of a Research Aim

The role of diametrical cup deformation as a factor to unsatisfactory implant performance has not been widely reported. The aim of this thesis was to gain an understanding of the diametrical deformation behaviour of acetabular cups and shells following impaction into the reamed acetabulum. The influence of a range of factors on deformation was investigated to ascertain if cup and shell deformation may be high enough to potentially contribute to early failure and high wear rates in metal-on-metal implants.

Note: Extracted with permission from thesis titled “T he Impact And Deformation Of Press-Fit Metal Acetabular Components ” produced by Dr H Hothi of previously Queen Mary University of London.

Research Objectives

Where a research aim specifies what your study will answer, research objectives specify how your study will answer it.

They divide your research aim into several smaller parts, each of which represents a key section of your research project. As a result, almost all research objectives take the form of a numbered list, with each item usually receiving its own chapter in a dissertation or thesis.

Following the example of the research aim shared above, here are it’s real research objectives as an example:

Example of a Research Objective

• Develop finite element models using explicit dynamics to mimic mallet blows during cup/shell insertion, initially using simplified experimentally validated foam models to represent the acetabulum.
• Investigate the number, velocity and position of impacts needed to insert a cup.
• Determine the relationship between the size of interference between the cup and cavity and deformation for different cup types.
• Investigate the influence of non-uniform cup support and varying the orientation of the component in the cavity on deformation.
• Examine the influence of errors during reaming of the acetabulum which introduce ovality to the cavity.
• Determine the relationship between changes in the geometry of the component and deformation for different cup designs.
• Develop three dimensional pelvis models with non-uniform bone material properties from a range of patients with varying bone quality.
• Use the key parameters that influence deformation, as identified in the foam models to determine the range of deformations that may occur clinically using the anatomic models and if these deformations are clinically significant.

It’s worth noting that researchers sometimes use research questions instead of research objectives, or in other cases both. From a high-level perspective, research questions and research objectives make the same statements, but just in different formats.

Taking the first three research objectives as an example, they can be restructured into research questions as follows:

Restructuring Research Objectives as Research Questions

• Can finite element models using simplified experimentally validated foam models to represent the acetabulum together with explicit dynamics be used to mimic mallet blows during cup/shell insertion?
• What is the number, velocity and position of impacts needed to insert a cup?
• What is the relationship between the size of interference between the cup and cavity and deformation for different cup types?

Difference Between Aims and Objectives

Hopefully the above explanations make clear the differences between aims and objectives, but to clarify:

• The research aim focus on what the research project is intended to achieve; research objectives focus on how the aim will be achieved.
• Research aims are relatively broad; research objectives are specific.
• Research aims focus on a project’s long-term outcomes; research objectives focus on its immediate, short-term outcomes.
• A research aim can be written in a single sentence or short paragraph; research objectives should be written as a numbered list.

How to Write Aims and Objectives

Before we discuss how to write a clear set of research aims and objectives, we should make it clear that there is no single way they must be written. Each researcher will approach their aims and objectives slightly differently, and often your supervisor will influence the formulation of yours on the basis of their own preferences.

Regardless, there are some basic principles that you should observe for good practice; these principles are described below.

Your aim should be made up of three parts that answer the below questions:

• Why is this research required?
• What is this research about?
• How are you going to do it?

The easiest way to achieve this would be to address each question in its own sentence, although it does not matter whether you combine them or write multiple sentences for each, the key is to address each one.

The first question, why , provides context to your research project, the second question, what , describes the aim of your research, and the last question, how , acts as an introduction to your objectives which will immediately follow.

Scroll through the image set below to see the ‘why, what and how’ associated with our research aim example.

Note: Your research aims need not be limited to one. Some individuals per to define one broad ‘overarching aim’ of a project and then adopt two or three specific research aims for their thesis or dissertation. Remember, however, that in order for your assessors to consider your research project complete, you will need to prove you have fulfilled all of the aims you set out to achieve. Therefore, while having more than one research aim is not necessarily disadvantageous, consider whether a single overarching one will do.

Research Objectives

Each of your research objectives should be SMART :

• Specific – is there any ambiguity in the action you are going to undertake, or is it focused and well-defined?
• Measurable – how will you measure progress and determine when you have achieved the action?
• Achievable – do you have the support, resources and facilities required to carry out the action?
• Relevant – is the action essential to the achievement of your research aim?
• Timebound – can you realistically complete the action in the available time alongside your other research tasks?

In addition to being SMART, your research objectives should start with a verb that helps communicate your intent. Common research verbs include:

Table of Research Verbs to Use in Aims and Objectives

Last, format your objectives into a numbered list. This is because when you write your thesis or dissertation, you will at times need to make reference to a specific research objective; structuring your research objectives in a numbered list will provide a clear way of doing this.

To bring all this together, let’s compare the first research objective in the previous example with the above guidance:

Checking Research Objective Example Against Recommended Approach

Research Objective:

1. Develop finite element models using explicit dynamics to mimic mallet blows during cup/shell insertion, initially using simplified experimentally validated foam models to represent the acetabulum.

Checking Against Recommended Approach:

Q: Is it specific? A: Yes, it is clear what the student intends to do (produce a finite element model), why they intend to do it (mimic cup/shell blows) and their parameters have been well-defined ( using simplified experimentally validated foam models to represent the acetabulum ).

Q: Is it measurable? A: Yes, it is clear that the research objective will be achieved once the finite element model is complete.

Q: Is it achievable? A: Yes, provided the student has access to a computer lab, modelling software and laboratory data.

Q: Is it relevant? A: Yes, mimicking impacts to a cup/shell is fundamental to the overall aim of understanding how they deform when impacted upon.

Q: Is it timebound? A: Yes, it is possible to create a limited-scope finite element model in a relatively short time, especially if you already have experience in modelling.

Q: Does it start with a verb? A: Yes, it starts with ‘develop’, which makes the intent of the objective immediately clear.

Q: Is it a numbered list? A: Yes, it is the first research objective in a list of eight.

Mistakes in Writing Research Aims and Objectives

1. making your research aim too broad.

Having a research aim too broad becomes very difficult to achieve. Normally, this occurs when a student develops their research aim before they have a good understanding of what they want to research. Remember that at the end of your project and during your viva defence , you will have to prove that you have achieved your research aims; if they are too broad, this will be an almost impossible task. In the early stages of your research project, your priority should be to narrow your study to a specific area. A good way to do this is to take the time to study existing literature, question their current approaches, findings and limitations, and consider whether there are any recurring gaps that could be investigated .

Note: Achieving a set of aims does not necessarily mean proving or disproving a theory or hypothesis, even if your research aim was to, but having done enough work to provide a useful and original insight into the principles that underlie your research aim.

2. Making Your Research Objectives Too Ambitious

Be realistic about what you can achieve in the time you have available. It is natural to want to set ambitious research objectives that require sophisticated data collection and analysis, but only completing this with six months before the end of your PhD registration period is not a worthwhile trade-off.

3. Formulating Repetitive Research Objectives

Each research objective should have its own purpose and distinct measurable outcome. To this effect, a common mistake is to form research objectives which have large amounts of overlap. This makes it difficult to determine when an objective is truly complete, and also presents challenges in estimating the duration of objectives when creating your project timeline. It also makes it difficult to structure your thesis into unique chapters, making it more challenging for you to write and for your audience to read.

Fortunately, this oversight can be easily avoided by using SMART objectives.

Hopefully, you now have a good idea of how to create an effective set of aims and objectives for your research project, whether it be a thesis, dissertation or research paper. While it may be tempting to dive directly into your research, spending time on getting your aims and objectives right will give your research clear direction. This won’t only reduce the likelihood of problems arising later down the line, but will also lead to a more thorough and coherent research project.

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• Indian J Anaesth
• v.66(1); 2022 Jan

Dissertation writing in post graduate medical education

Department of Anaesthesiology, Dr. B R Ambedkar Medical College, Bengaluru, Karnataka, India

Mridul M Panditrao

1 Department of Anaesthesiology and Intensive Care, Adesh Institute of Medical Sciences and Research (AIMSR), Bathinda, Punjab, India

2 Department of Anaesthesiology, Kasturba Medical College, Mangalore, Manipal Academy of Higher Education, Manipal, India

Sukhminder Jit Singh Bajwa

3 Department of Anaesthesiology and Intensive Care, Gian Sagar Medical College and Hospital, Patiala, Punjab, India

Nishant Sahay

4 Department of Anaesthesiology, All India Institute of Medical Sciences, Patna, Bihar, India

Thrivikrama Padur Tantry

5 Department of Anaesthesiology, A J Institute of Medical Sciences and Research Centre, Kuntikana, Mangalore, Karnataka, India

Associated Data

A dissertation is a practical exercise that educates students about basics of research methodology, promotes scientific writing and encourages critical thinking. The National Medical Commission (India) regulations make assessment of a dissertation by a minimum of three examiners mandatory. The candidate can appear for the final examination only after acceptance of the dissertation. An important role in a dissertation is that of the guide who has to guide his protégés through the process. This manuscript aims to assist students and guides on the basics of conduct of a dissertation and writing the dissertation. For students who will ultimately become researchers, a dissertation serves as an early exercise. Even for people who may never do research after their degree, a dissertation will help them discern the merits of new treatment options available in literature for the benefit of their patients.

INTRODUCTION

The zenith of clinical residency is the completion of the Master's Dissertation, a document formulating the result of research conducted by the student under the guidance of a guide and presenting and publishing the research work. Writing a proper dissertation is most important to present the research findings in an acceptable format. It is also reviewed by the examiners to determine a part of the criteria for the candidate to pass the Masters’ Degree Examination.

The predominant role in a dissertation is that of the guide who has to mentor his protégés through the process by educating them on research methodology, by: (i) identifying a pertinent and topical research question, (ii) formulating the “type” of study and the study design, (iii) selecting the sample population, (iv) collecting and collating the research data accurately, (v) analysing the data, (vi) concluding the research by distilling the outcome, and last but not the least (vii) make the findings known by publication in an acceptable, peer-reviewed journal.[ 1 ] The co-guide could be a co-investigator from another department related to the study topic, and she/he will play an equivalent role in guiding the student.

Research is a creative and systematic work undertaken to increase the stock of knowledge.[ 2 ] This work, known as a study may be broadly classified into two groups in a clinical setting:

• Trials: Here the researcher intervenes to either prevent a disease or to treat it.
• Observational studies: Wherein the investigator makes no active intervention and merely observes the patients or subjects allocated the treatment based on clinical decisions.[ 3 ]

The research which is described in a dissertation needs to be presented under the following headings: Introduction, Aim of the Study, Description of devices if any or pharmacology of drugs, Review of Literature, Material and Methods, Observations and Results, Discussion, Conclusions, Limitations of the study, Bibliography, Proforma, Master chart. Some necessary certificates from the guide and the institute are a requirement in certain universities. The students often add an acknowledgement page before the details of their dissertation proper. It is their expression of gratitude to all of those who they feel have been directly or indirectly helpful in conduct of the study, data analysis, and finally construction of the dissertation.

Framing the research question (RQ)

It is the duty of the teacher to suggest suitable research topics to the residents, based on resources available, feasibility and ease of conduct at the centre. Using the FINER criteria, the acronym for feasibility, topical interest, novelty, ethicality and relevance would be an excellent way to create a correct RQ.[ 4 ]

The PICOT method which describes the patient, intervention, comparison, outcome and time, would help us narrow down to a specific and well-formulated RQ.[ 5 , 6 ] A good RQ leads to the derivation of a research hypothesis, which is an assumption or prediction of the outcome that will be tested by the research. The research topic could be chosen from among the routine clinical work regarding clinical management, use of drugs e.g., vasopressors to prevent hypotension or equipment such as high flow nasal oxygen to avoid ventilation.

Review of literature

To gather this information may be a difficult task for a fresh trainee however, a good review of the available literature is a tool to identify and narrow down a good RQ and generate a hypothesis. Literature sources could be primary (clinical trials, case reports), secondary (reviews, meta-analyses) or tertiary (e.g., reference books, compilations). Methods of searching literature could be manual (journals) or electronic (online databases), by looking up references or listed citations in existing articles. Electronic database searches are made through the various search engines available online e.g., scholar.google.com, National Library of Medicine (NLM) website, clinical key app and many more. Advanced searches options may help narrow down the search results to those that are relevant for the student. This could be based on synthesising keywords from the RQ, or by searching for phrases, Boolean operators, or utilising filters.

After choosing the topic, an apt and accurate title has to be chosen. This should be guided by the use of Medical Subject Headings (MeSH) terminology from the NLM, which is used for indexing, cataloguing, and searching of biomedical and health-related information.[ 7 ] The dissertation requires a detailed title which may include the objective of the study, key words and even the PICOT components. One may add the study design in the title e.g. “a randomised cross over study” or “an observational analytical study” etc.

Aim and the objectives

The Aims and the Objectives of the research study have to be listed clearly, before initiating the study.[ 8 ] “Gaps” or deficiencies in existing knowledge should be clearly cited. The Aim by definition is a statement of the expected outcome, while the Objectives (which might be further classed into primary and secondary based on importance) should be specific, measurable, achievable, realistic or relevant, time-bound and challenging; in short, “SMART!” To simplify, the aim is a statement of intent, in terms of what we hope to achieve at the end of the project. Objectives are specific, positive statements of measurable outcomes, and are a list of steps that will be taken to achieve the outcome.[ 9 ] Aim of a dissertation, for example, could be to know which of two nerve block techniques is better. To realise this aim, comparing the duration of postoperative analgesia after administration of the block by any measurable criteria, could be an objective, such as the time to use of first rescue analgesic drug. Similarly, total postoperative analgesic drug consumption may form a secondary outcome variable as it is also measurable. These will generate data that may be used for analysis to realise the main aim of the study.

Inclusion and exclusions

The important aspect to consider after detailing when and how the objectives will be measured is documenting the eligibility criteria for inclusion of participants. The exclusion criteria must be from among the included population/patients only. e.g., If only American Society of Anesthesiologists (ASA) I and II are included, then ASA III and IV cannot be considered as exclusion criteria, since they were never a part of the study. The protocol must also delineate the setting of the study, locations where data would be collected, and specify duration of conduct of the dissertation. A written informed consent after explaining the aim, objectives and methodology of the study is legally mandatory before embarking upon any human study. The study should explicitly clarify whether it is a retrospective or a prospective study, where the study is conducted and the duration of the study.

Sample size: The sample subjects in the study should be representative of the population upon whom the inference has to be drawn. Sampling is the process of selecting a group of representative people from a larger population and subjecting them for the research.[ 10 ] The sample size represents a number, beyond which the addition of population is unlikely to change the conclusion of the study. The sample size is calculated taking into consideration the primary outcome criteria, confidence interval (CI), power of the study, and the effect size the researcher wishes to observe in the primary objective of the study. Hence a typical sample size statement can be - “Assuming a duration of analgesia of 150 min and standard deviation (SD) of 15 min in first group, keeping power at 80% and CIs at 95% (alpha error at 0.05), a sample of 26 patients would be required to detect a minimum difference (effect size) of 30% in the duration of analgesia between the two groups. Information regarding the different sampling methods and sample size calculations may be found in the Supplementary file 1 .

Any one research question may be answered using a number of research designs.[ 11 ] Research designs are often described as either observational or experimental. The various research designs may be depicted graphically as shown in Figure 1 .

Graphical description of available research designs

The observational studies lack “the three cornerstones of experimentation” – controls, randomisation, and replication. In an experimental study on the other hand, in order to assess the effect of treatment intervention on a participant, it is important to compare it with subjects similar to each other but who have not been given the studied treatment. This group, also called the control group, may help distinguish the effect of the chosen intervention on outcomes from effects caused by other factors, such as the natural history of disease, placebo effects, or observer or patient expectations.

All the proposed dissertations must be submitted to the scientific committee for any suggestion regarding the correct methodology to be followed, before seeking ethical committee approval.

Ethical considerations

Ethical concerns are an important part of the research project, right from selection of the topic to the dissertation writing. It must be remembered, that the purpose of a dissertation given to a post-graduate student is to guide him/her through the process by educating them on the very basics of research methodology. It is therefore not imperative that the protégés undertake a complicated or risky project. If research involves human or animal subjects, drugs or procedures, research ethics guidelines as well as drug control approvals have to be obtained before tabling the proposal to the Institutional Ethics Committee (IEC). The roles, responsibilities and composition of the Ethics Committee has been specified by the Directorate General of Health Services, Government of India. Documented approval of the Ethics committee is mandatory before any subject can be enroled for any dissertation in India. Even retrospective studies require approval from the IEC. Details of this document is available at: https://cdsco.gov.in/opencms/resources/UploadCDSCOWeb/2018/UploadEthicsRegistration/Applmhrcrr.pdf .

The candidate and the guide are called to present their proposal before the committee. The ethical implications, risks and management, subjects’ rights and responsibilities, informed consent, monetary aspects, the research and analysis methods are all discussed. The patient safety is a topmost priority and any doubts of the ethical committee members should be explained in medically layman's terms. The dissertation topics should be listed as “Academic clinical trials” and must involve only those drugs which are already approved by the Drugs Controller General of India. More commonly, the Committee suggests rectifications, and then the researchers have to resubmit the modified proposal after incorporating the suggestions, at the next sitting of the committee or seek online approval, as required. At the conclusion of the research project, the ethics committee has to be updated with the findings and conclusions, as well as when it is submitted for publication. Any deviation from the approved timeline, as well as the research parameters has to be brought to the attention of the IEC immediately, and re-approval sought.

Clinical trial registration

Clinical Trial Registry of India (CTRI) is a free online searchable system for prospective registration of all clinical studies conducted in India. It is owned and managed by the National Institute of Medical Statistics, a division of Indian Council of Medical Research, Government of India. Registration of clinical trials will ensure transparency, accountability and accessibility of trials and their results to all potential beneficiaries.

After the dissertation proposal is passed by the scientific committee and IEC, it may be submitted for approval of trial registration to the CTRI. The student has to create a login at the CTRI website, and submit all the required data with the help of the guides. After submission, CTRI may ask for corrections, clarifications or changes. Subject enrolment and the actual trial should begin only after the CTRI approval.

Randomisation

In an experimental study design, the method of randomisation gives every subject an equal chance to get selected in any group by preventing bias. Primarily, three basic types employed in post-graduate medical dissertations are simple randomisation, block randomisation and stratified randomisation. Simple randomisation is based upon a single sequence of random assignments such as flipping a coin, rolling of dice (above 3 or below 3), shuffling of cards (odd or even) to allocate into two groups. Some students use a random number table found in books or use computer-generated random numbers. There are many random number generators, randomisation programs as well as randomisation services available online too. ( https://www-users.york.ac.uk/~mb55/guide/randsery.htm ).

There are many applications which generate random number sequences and a research student may use such computer-generated random numbers [ Figure 2 ]. Simple randomisation has higher chances of unequal distribution into the two groups, especially when sample sizes are low (<100) and thus block randomisation may be preferred. Details of how to do randomisation along with methods of allocation concealment may be found in Supplementary file 2 .

Figure depicting how to do block randomisation using online resources. (a) generation of a random list (b) transfer of the list to an MS excel file

Allocation concealment

If it is important in a study to generate a random sequence of intervention, it is also important for this sequence to be concealed from all stake-holders to prevent any scope of bias.[ 12 ] Allocation concealment refers to the technique used to implement a random sequence for allocation of intervention, and not to generate it.[ 13 ] In an Indian post-graduate dissertation, the sequentially numbered, opaque, sealed envelopes (SNOSE) technique is commonly used [ Supplementary file 2 ].

To minimise the chances of differential treatment allocation or assessments of outcomes, it is important to blind as many individuals as possible in the trial. Blinding is not an all-or-none phenomenon. Thus, it is very desirable to explicitly state in the dissertation, which individuals were blinded, how they achieved blinding and whether they tested the success of blinding.

Commonly used terms for blinding are

• Single blinding: Masks the participants from knowing which intervention has been given.
• Double blinding: Blinds both the participants as well as researchers to the treatment allocation.
• Triple blinding: By withholding allocation information from the subjects, researchers, as well as data analysts. The specific roles of researchers involved in randomisation, allocation concealment and blinding should be stated clearly in the dissertation.

Data which can be measured as numbers are called quantitative data [ Table 1 ]. Studies which emphasise objective measurements to generate numerical data and then apply statistical and mathematical analysis constitute quantitative research. Qualitative research on the other hand focuses on understanding people's beliefs, experiences, attitudes, behaviours and thus these generate non-numerical data called qualitative data, also known as categorical data, descriptive data or frequency counts. Importance of differentiating data into qualitative and quantitative lies in the fact that statistical analysis as well as the graphical representation may be very different.

Data collection types

In order to obtain data from the outcome variable for the purpose of analysis, we need to design a study which would give us the most valid information. A valid data or measurement tool, is the degree to which the tool measures what it claims to measure. For example, appearance of end tidal carbon dioxide waveform is a more valid measurement to assess correct endotracheal tube placement than auscultation of breath sounds on chest inflation.

The compilation of all data in a ‘Master Chart’ is a necessary step for planning, facilitating and appropriate preparation and processing of the data for analysis. It is a complete set of raw research data arranged in a systematic manner forming a well-structured and formatted, computable data matrix/database of the research to facilitate data analysis. The master chart is prepared as a Microsoft Excel sheet with the appropriate number of columns depicting the variable parameters for each individual subjects/respondents enlisted in the rows.

Statistical analysis

The detailed statistical methodology applied to analyse the data must be stated in the text under the subheading of statistical analysis in the Methods section. The statistician should be involved in the study during the initial planning stage itself. Following four steps have to be addressed while planning, performing and text writing of the statistical analysis part in this section.

Step 1. How many study groups are present? Whether analysis is for an unpaired or paired situation? Whether the recorded data contains repeated measurements? Unpaired or paired situations decide again on the choice of a test. The latter describes before and after situations for collected data (e.g. Heart rate data ‘before’ and ‘after’ spinal anaesthesia for a single group). Further, data should be checked to find out whether they are from repeated measurements (e.g., Mean blood pressure at 0, 1 st , 2 nd , 5 th , 10 th minutes and so on) for a group. Different types of data are commonly encountered in a dissertation [ Supplementary file 3A ].

Step 2. Does the data follow a normal distribution?[ 14 ]

Each study group as well as every parameter has to be checked for distribution analysis. This step will confirm whether the data of a particular group is normally distributed (parametric data) or does not follow the normal distribution (non-parametric data); subsequent statistical test selection mainly depends on the results of the distribution analysis. For example, one may choose the Student's’ test instead of the ‘Mann-Whitney U’ for non-parametric data, which may be incorrect. Each study group as well as every parameter has to be checked for distribution analysis [ Supplementary File 3B ].

Step 3. Calculation of measures of central tendency and measures of variability.

Measures of central tendency mainly include mean, median and mode whereas measures of variability include range, interquartile range (IQR), SD or variance not standard error of mean. Depending on Step 2 findings, one needs to make the appropriate choice. Mean and SD/variance are more often for normally distributed and median with IQR are the best measure for not normal (skewed) distribution. Proportions are used to describe the data whenever the sample size is ≥100. For a small sample size, especially when it is approximately 25-30, describe the data as 5/25 instead of 20%. Software used for statistical analysis automatically calculates the listed step 3 measures and thus makes the job easy.

Step 4. Which statistical test do I choose for necessary analysis?

Choosing a particular test [ Figure 3 ] is based on orderly placed questions which are addressed in the dissertation.[ 15 ]

Chosing a statistical test, (a). to find a difference between the groups of unpaired situations, (b). to find a difference between the groups of paired situations, (c). to find any association between the variables, (d). to find any agreement between the assessment techniques. ANOVA: Analysis of Variance. Reproduced with permission from Editor of Indian Journal of Ophthalmology, and the author, Dr Barun Nayak[ 15 ]

• Is there a difference between the groups of unpaired situations?
• Is there a difference between the groups of paired situations?
• Is there any association between the variables?
• Is there any agreement between the assessment techniques?

Perform necessary analysis using user-friendly software such as GraphPad Prism, Minitab or MedCalc,etc. Once the analysis is complete, appropriate writing in the text form is equally essential. Specific test names used to examine each part of the results have to be described. Simple listing of series of tests should not be done. A typical write-up can be seen in the subsequent sections of the supplementary files [Supplementary files 3C – E ]. One needs to state the level of significance and software details also.

Role of a statistician in dissertation and data analysis

Involving a statistician before planning a study design, prior to data collection, after data have been collected, and while data are analysed is desirable when conducting a dissertation. On the contrary, it is also true that self-learning of statistical analysis reduces the need for statisticians’ help and will improve the quality of research. A statistician is best compared to a mechanic of a car which we drive; he knows each element of the car, but it is we who have to drive it. Sometimes the statisticians may not be available for a student in an institute. Self-learning software tools, user-friendly statistical software for basic statistical analysis thus gain importance for students as well as guides. The statistician will design processes for data collection, gather numerical data, collect, analyse, and interpret data, identify the trends and relationships in data, perform statistical analysis and its interpretation, and finally assist in final conclusion writing.

Results are an important component of the dissertation and should follow clearly from the study objectives. Results (sometimes described as observations that are made by the researcher) should be presented after correct analysis of data, in an appropriate combination of text, charts, tables, graphs or diagrams. Decision has to be taken on each outcome; which outcome has to be presented in what format, at the beginning of writing itself. These should be statistically interpreted, but statistics should not surpass the dissertation results. The observations should always be described accurately and with factual or realistic values in results section, but should not be interpreted in the results section.

While writing, classification and reporting of the Results has to be done under five section paragraphs- population data, data distribution analysis, results of the primary outcome, results of secondary outcomes, any additional observations made such as a rare adverse event or a side effect (intended or unintended) or of any additional analysis that may have been done, such as subgroup analysis.

At each level, one may either encounter qualitative (n/N and %) or quantitative data (mean [SD], median [IQR] and so on.

In the first paragraph of Results while describing the population data, one has to write about included and excluded patients. One needs to cite the Consolidated Standards of Reporting Trials (CONSORT) flow chart to the text, at this stage. Subsequently, highlighting of age, sex, height, body mass index (BMI) and other study characteristics referring to the first table of ‘patients data’ should be considered. It is not desirable to detail all values and their comparison P values in the text again in population data as long as they are presented in a cited table. An example of this pattern can be seen in Supplementary file 3D .

In the second paragraph, one needs to explain how the data is distributed. It should be noted that, this is not a comparison between the study groups but represents data distribution for the individual study groups (Group A or Group B, separately)[ Supplementary file 3E ].

In the subsequent paragraph of Results , focused writing on results of the primary outcomes is very important. It should be attempted to mention most of the data outputs related to the primary outcomes as the study is concluded based on the results of this outcome analysis. The measures of central tendency and dispersion (Mean or median and SD or IQR etc., respectively), alongside the CIs, sample number and P values need to be mentioned. It should be noted that the CIs can be for the mean as well as for the mean difference and should not be interchanged. An example of this pattern can be seen in Supplementary file 3F .

A large number of the dissertations are guided for single primary outcome analysis, and also the results of multiple secondary outcomes are needed to be written. The primary outcome should be presented in detail, and secondary outcomes can be presented in tables or graphs only. This will help in avoiding a possible evaluator's fatigue. An example of this pattern can be seen in Supplementary file 3G .

In the last paragraph of the Results, mention any additional observations, such as a rare adverse event or side effect or describe the unexpected results. The results of any additional analysis (subgroup analysis) then need to be described too. An example of this pattern can be seen in Supplementary file 3H .

The most common error observed in the Results text is duplication of the data and analytical outputs. While using the text for summarising the results, at each level, it should not be forgotten to cite the table or graph but the information presented in a table should not be repeated in the text. Further, results should not be given to a greater degree of accuracy than that of the measurement. For example, mean (SD) age need to be presented as 34.5 (11.3) years instead of 34.5634 (11.349). The latter does not carry any additional information and is unnecessary. The actual P values need to be mentioned. The P value should not be simply stated as ‘ P < 0.05’; P value should be written with the actual numbers, such as ‘ P = 0.021’. The symbol ‘<’ should be used only when actual P value is <0.001 or <0.0001. One should try avoiding % calculations for a small sample especially when n < 100. The sample size calculation is a part of the methodology and should not be mentioned in the Results section.

The use of tables will help present actual data values especially when in large numbers. The data and their relationships can be easily understood by an appropriate table and one should avoid overwriting of results in the text format. All values of sample size, central tendency, dispersions, CIs and P value are to be presented in appropriate columns and rows. Preparing a dummy table for all outcomes on a rough paper before proceeding to Microsoft Excel may be contemplated. Appropriate title heading (e.g., Table 1 . Study Characteristics), Column Headings (e.g., Parameter studied, P values) should be presented. A footnote should be added whenever necessary. For outputs, where statistically significant P values are recorded, the same should be highlighted using an asterisk (*) symbol and the same *symbol should be cited in the footnote describing its value (e.g., * P < 0.001) which is self-explanatory for statistically significance. One should not use abbreviations such as ‘NS’ or ‘Sig’ for describing (non-) significance. Abbreviations should be described for all presented tables. A typical example of a table can be seen in Figure 4 .

Example of presenting a table

Graphical images

Similar to tables, the graphs and diagrams give a bird's-eye view of the entire data and therefore may easily be understood. bar diagrams (simple, multiple or component), pie charts, line diagrams, pictograms and spot maps suit qualitative data more whereas the histograms, frequency polygons, cumulative frequency, polygon scatter diagram, box and whisker plots and correlation diagrams are used to depict quantitative data. Too much presentation of graphs and images, selection of inappropriate or interchanging of graphs, unnecessary representation of three-dimensional graph for one-dimensional graphs, disproportionate sizes of length and width and incorrect scale and labelling of an axis should be avoided. All graphs should contain legends, abbreviation descriptions and a footnote. Appropriate labelling of the x - and the y -axis is also essential. Priori decided scale for axis data should be considered. The ‘error bar’ represents SDs or IQRs in the graphs and should be used irrespective of whether they are bar charts or line graphs. Not showing error bars in a graphical image is a gross mistake. An error bar can be shown on only one side of the line graph to keep it simple. A typical example of a graphical image can be seen in Figure 5 . The number of subjects (sample) is to be mentioned for each time point on the x -axis. An asterisk (*) needs to be put for data comparisons having statistically significant P value in the graph itself and they are self-explanatory with a ‘stand-alone’ graph.

Example of an incorrect (a) and correct (b) image

Once the results have been adequately analysed and described, the next step is to draw conclusions from the data and study. The main goal is to defend the work by staging a constructive debate with the literature.[ 16 ] Generally, the length of the ‘ Discussion ’ section should not exceed the sum of other sections (introduction, material and methods, and results).[ 17 ] Here the interpretation, importance/implications, relevance, limitations of the results are elaborated and should end in recommendations.

It is advisable to start by mentioning the RQ precisely, summarising the main findings without repeating the entire data or results again. The emphasis should be on how the results correlate with the RQ and the implications of these results, with the relevant review of literature (ROL). Do the results coincide with and add anything to the prevalent knowledge? If not, why not? It should justify the differences with plausible explanation. Ultimately it should be made clear, if the study has been successful in making some contribution to the existing evidence. The new results should not be introduced and any exaggerated deductions which cannot be corroborated by the outcomes should not be made.

The discussion should terminate with limitations of the study,[ 17 ] mentioned magnanimously. Indicating limitations of the study reflects objectivity of the authors. It should not enlist any errors, but should acknowledge the constraints and choices in designing, planning methodology or unanticipated challenges that may have cropped up during the actual conduct of the study. However, after listing the limitations, the validity of results pertaining to the RQ may be emphasised again.

This section should convey the precise and concise message as the take home message. The work carried out should be summarised and the answer found to the RQ should be succinctly highlighted. One should not start dwelling on the specific results but mention the overall gain or insights from the observations, especially, whether it fills the gap in the existing knowledge if any. The impact, it may have on the existing knowledge and practices needs to be reiterated.

What to do when we get a negative result?

Sometimes, despite the best research framework, the results obtained are inconclusive or may even challenge a few accepted assumptions.[ 18 ] These are frequently, but inappropriately, termed as negative results and the data as negative data. Students must believe that if the study design is robust and valid, if the confounders have been carefully neutralised and the outcome parameters measure what they are intended to, then no result is a negative result. In fact, such results force us to critically re-evaluate our current understanding of concepts and knowledge thereby helping in better decision making. Studies showing lack of prolongation of the apnoea desaturation safety periods at lower oxygen flows strengthened belief in the difficult airway guidelines which recommend nasal insufflations with at least 15 L/min oxygen.[ 19 , 20 , 21 ]

Publishing the dissertation work

There are many reporting guidelines based upon the design of research. These are a checklist, flow diagram, or structured text to guide authors in reporting a specific type of research, developed using explicit methodology. The CONSORT[ 22 ] and Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) initiatives,[ 23 ] both included in the Enhancing the Quality and Transparency of Health Research (EQUATOR) international network, have elaborated appropriate suggestions to improve the transparency, clarity and completeness of scientific literature [ Figure 6 ].

Equator publishing tree

All authors are advised to follow the CONSORT/STROBE checklist attached as Supplementary file 4 , when writing and reporting their dissertation.

For most dissertations in Anaesthesiology, the CONSORT, STROBE, Standards for Reporting Diagnostic accuracy studies (STARD) or REporting recommendations for tumour MARKer prognostic studies (REMARK) guidelines would suffice.

Abstract and Summary

These two are the essential sections of a dissertation.

It should be at the beginning of the manuscript, after the title page and acknowledgments, but before the table of contents. The preparation varies as per the University guidelines, but generally ranges between 150 to 300 words. Although it comes at the very beginning of the thesis, it is the last part one writes. It must not be a ‘copy-paste job’ from the main manuscript, but well thought out miniaturisation, giving the overview of the entire text. As a rule, there should be no citation of references here.

Logically, it would have four components starting with aims, methods, results, and conclusion. One should begin the abstract with the research question/objectives precisely, avoiding excessive background information. Adjectives like, evaluate, investigate, test, compare raise the curiosity quotient of the reader. This is followed by a brief methodology highlighting only the core steps used. There is no need of mentioning the challenges, corrections, or modifications, if any. Finally, important results, which may be restricted to fulfilment (or not), of the primary objective should be mentioned. Abstracts end with the main conclusion stating whether a specific answer to the RQ was found/not found. Then recommendations as a policy statement or utility may be made taking care that it is implementable.

Keywords may be included in the abstract, as per the recommendations of the concerned university. The keywords are primarily useful as markers for future searches. Lastly, the random reader using any search engine may use these, and the identifiability is increased.

The summary most often, is either the last part of the Discussion or commonly, associated with the conclusions (Summary and Conclusions). Repetition of introduction, whole methodology, and all the results should be avoided. Summary, if individually written, should not be more than 150 to 300 words. It highlights the research question, methods used to investigate it, the outcomes/fallouts of these, and then the conclusion part may start.

References/bibliography

Writing References serves mainly two purposes. It is the tacit acknowledgement of the fact that someone else's written words or their ideas or their intellectual property (IP) are used, in part or in toto , to avoid any blame of plagiarism. It is to emphasise the circumspective and thorough literature search that has been carried out in preparation of the work.

Vancouver style for referencing is commonly used in biomedical dissertation writing. A reference list contains details of the works cited in the text of the document. (e.g. book, journal article, pamphlet, government reports, conference material, internet site). These details must include sufficient details so that others may locate and access those references.[ 24 ]

How much older the references can be cited, depends upon the university protocol. Conventionally accepted rule is anywhere between 5-10 years. About 85% of references should be dispersed in this time range. Remaining 15%, which may include older ones if they deal with theories, historical aspects, and any other factual content. Rather than citing an entire book, it is prudent to concentrate on the chapter or subsection of the text. There are subjective variations between universities on this matter. But, by and large, these are quoted as and when deemed necessary and with correct citation.

Bibliography is a separate list from the reference list and should be arranged alphabetically by writing name of the ‘author or title’ (where no author name is given) in the Vancouver style.

There are different aspects of writing the references.[ 24 ]

Citing the reference in the form of a number in the text. The work of other authors referred in the manuscript should be given a unique number and quoted. This is done in the order of their appearance in the text in chronological order by using Arabic numerals. The multiple publications of same author shall be written individually. If a reference article has more than six authors, all six names should be written, followed by “ et al .” to be used in lieu of other author names. It is desirable to write the names of the journals in abbreviations as per the NLM catalogue. Examples of writing references from the various sources may be found in the Supplementary file 5 .

Both the guide and the student have to work closely while searching the topic initially and also while finalising the submission of the dissertation. But the role of the guide in perusing the document in detail, and guiding the candidate through the required corrections by periodic updates and discussions cannot be over-emphasised.

Assessment of dissertations

Rarely, examiners might reject a dissertation for failure to choose a contemporary topic, a poor review of literature, defective methodology, biased analysis or incorrect conclusions. If these cannot be corrected satisfactorily, it will then be back to the drawing board for the researchers, who would have to start from scratch to redesign the study, keeping the deficiencies in mind this time.

Before submission, dissertation has to be run through “plagiarism detector” software, such as Turnitin or Grammarly to ensure that plagiarism does not happen even unwittingly. Informal guidelines state that the percentage plagiarism picked up by these tools should be <10%.

No work of art is devoid of mistakes/errors. Logically, a dissertation, being no exception, may also have errors. Our aim, is to minimise them.

The dissertation is an integral part in the professional journey of any medical post-graduate student. It is also an important responsibility for a guide to educate his protégé, the basics of research methodology through the process. Searching for a gap in literature and identification of a pertinent research question is the initial step. Careful planning of the study design is a vitally important aspect. After the conduct of study, writing the dissertation is an art for which the student often needs guidance. A good dissertation is a good description of a meticulously conducted study under the different headings described, utilising the various reporting guidelines. By avoiding some common errors as discussed in this manuscript, a good dissertation can result in a very fruitful addition to medical literature.

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Conflicts of interest.

There are no conflicts of interest.

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Scientific Method

Science is an enormously successful human enterprise. The study of scientific method is the attempt to discern the activities by which that success is achieved. Among the activities often identified as characteristic of science are systematic observation and experimentation, inductive and deductive reasoning, and the formation and testing of hypotheses and theories. How these are carried out in detail can vary greatly, but characteristics like these have been looked to as a way of demarcating scientific activity from non-science, where only enterprises which employ some canonical form of scientific method or methods should be considered science (see also the entry on science and pseudo-science ). Others have questioned whether there is anything like a fixed toolkit of methods which is common across science and only science. Some reject privileging one view of method as part of rejecting broader views about the nature of science, such as naturalism (Dupré 2004); some reject any restriction in principle (pluralism).

Scientific method should be distinguished from the aims and products of science, such as knowledge, predictions, or control. Methods are the means by which those goals are achieved. Scientific method should also be distinguished from meta-methodology, which includes the values and justifications behind a particular characterization of scientific method (i.e., a methodology) — values such as objectivity, reproducibility, simplicity, or past successes. Methodological rules are proposed to govern method and it is a meta-methodological question whether methods obeying those rules satisfy given values. Finally, method is distinct, to some degree, from the detailed and contextual practices through which methods are implemented. The latter might range over: specific laboratory techniques; mathematical formalisms or other specialized languages used in descriptions and reasoning; technological or other material means; ways of communicating and sharing results, whether with other scientists or with the public at large; or the conventions, habits, enforced customs, and institutional controls over how and what science is carried out.

While it is important to recognize these distinctions, their boundaries are fuzzy. Hence, accounts of method cannot be entirely divorced from their methodological and meta-methodological motivations or justifications, Moreover, each aspect plays a crucial role in identifying methods. Disputes about method have therefore played out at the detail, rule, and meta-rule levels. Changes in beliefs about the certainty or fallibility of scientific knowledge, for instance (which is a meta-methodological consideration of what we can hope for methods to deliver), have meant different emphases on deductive and inductive reasoning, or on the relative importance attached to reasoning over observation (i.e., differences over particular methods.) Beliefs about the role of science in society will affect the place one gives to values in scientific method.

The issue which has shaped debates over scientific method the most in the last half century is the question of how pluralist do we need to be about method? Unificationists continue to hold out for one method essential to science; nihilism is a form of radical pluralism, which considers the effectiveness of any methodological prescription to be so context sensitive as to render it not explanatory on its own. Some middle degree of pluralism regarding the methods embodied in scientific practice seems appropriate. But the details of scientific practice vary with time and place, from institution to institution, across scientists and their subjects of investigation. How significant are the variations for understanding science and its success? How much can method be abstracted from practice? This entry describes some of the attempts to characterize scientific method or methods, as well as arguments for a more context-sensitive approach to methods embedded in actual scientific practices.

1. Overview and organizing themes

2. historical review: aristotle to mill, 3.1 logical constructionism and operationalism, 3.2. h-d as a logic of confirmation, 3.3. popper and falsificationism, 3.4 meta-methodology and the end of method, 4. statistical methods for hypothesis testing, 5.1 creative and exploratory practices.

• 5.2 Computer methods and the ‘new ways’ of doing science

6.1 “The scientific method” in science education and as seen by scientists

6.2 privileged methods and ‘gold standards’, 6.3 scientific method in the court room, 6.4 deviating practices, 7. conclusion, other internet resources, related entries.

This entry could have been given the title Scientific Methods and gone on to fill volumes, or it could have been extremely short, consisting of a brief summary rejection of the idea that there is any such thing as a unique Scientific Method at all. Both unhappy prospects are due to the fact that scientific activity varies so much across disciplines, times, places, and scientists that any account which manages to unify it all will either consist of overwhelming descriptive detail, or trivial generalizations.

The choice of scope for the present entry is more optimistic, taking a cue from the recent movement in philosophy of science toward a greater attention to practice: to what scientists actually do. This “turn to practice” can be seen as the latest form of studies of methods in science, insofar as it represents an attempt at understanding scientific activity, but through accounts that are neither meant to be universal and unified, nor singular and narrowly descriptive. To some extent, different scientists at different times and places can be said to be using the same method even though, in practice, the details are different.

Whether the context in which methods are carried out is relevant, or to what extent, will depend largely on what one takes the aims of science to be and what one’s own aims are. For most of the history of scientific methodology the assumption has been that the most important output of science is knowledge and so the aim of methodology should be to discover those methods by which scientific knowledge is generated.

Science was seen to embody the most successful form of reasoning (but which form?) to the most certain knowledge claims (but how certain?) on the basis of systematically collected evidence (but what counts as evidence, and should the evidence of the senses take precedence, or rational insight?) Section 2 surveys some of the history, pointing to two major themes. One theme is seeking the right balance between observation and reasoning (and the attendant forms of reasoning which employ them); the other is how certain scientific knowledge is or can be.

Section 3 turns to 20 th century debates on scientific method. In the second half of the 20 th century the epistemic privilege of science faced several challenges and many philosophers of science abandoned the reconstruction of the logic of scientific method. Views changed significantly regarding which functions of science ought to be captured and why. For some, the success of science was better identified with social or cultural features. Historical and sociological turns in the philosophy of science were made, with a demand that greater attention be paid to the non-epistemic aspects of science, such as sociological, institutional, material, and political factors. Even outside of those movements there was an increased specialization in the philosophy of science, with more and more focus on specific fields within science. The combined upshot was very few philosophers arguing any longer for a grand unified methodology of science. Sections 3 and 4 surveys the main positions on scientific method in 20 th century philosophy of science, focusing on where they differ in their preference for confirmation or falsification or for waiving the idea of a special scientific method altogether.

In recent decades, attention has primarily been paid to scientific activities traditionally falling under the rubric of method, such as experimental design and general laboratory practice, the use of statistics, the construction and use of models and diagrams, interdisciplinary collaboration, and science communication. Sections 4–6 attempt to construct a map of the current domains of the study of methods in science.

As these sections illustrate, the question of method is still central to the discourse about science. Scientific method remains a topic for education, for science policy, and for scientists. It arises in the public domain where the demarcation or status of science is at issue. Some philosophers have recently returned, therefore, to the question of what it is that makes science a unique cultural product. This entry will close with some of these recent attempts at discerning and encapsulating the activities by which scientific knowledge is achieved.

Attempting a history of scientific method compounds the vast scope of the topic. This section briefly surveys the background to modern methodological debates. What can be called the classical view goes back to antiquity, and represents a point of departure for later divergences. [ 1 ]

We begin with a point made by Laudan (1968) in his historical survey of scientific method:

Perhaps the most serious inhibition to the emergence of the history of theories of scientific method as a respectable area of study has been the tendency to conflate it with the general history of epistemology, thereby assuming that the narrative categories and classificatory pigeon-holes applied to the latter are also basic to the former. (1968: 5)

To see knowledge about the natural world as falling under knowledge more generally is an understandable conflation. Histories of theories of method would naturally employ the same narrative categories and classificatory pigeon holes. An important theme of the history of epistemology, for example, is the unification of knowledge, a theme reflected in the question of the unification of method in science. Those who have identified differences in kinds of knowledge have often likewise identified different methods for achieving that kind of knowledge (see the entry on the unity of science ).

Different views on what is known, how it is known, and what can be known are connected. Plato distinguished the realms of things into the visible and the intelligible ( The Republic , 510a, in Cooper 1997). Only the latter, the Forms, could be objects of knowledge. The intelligible truths could be known with the certainty of geometry and deductive reasoning. What could be observed of the material world, however, was by definition imperfect and deceptive, not ideal. The Platonic way of knowledge therefore emphasized reasoning as a method, downplaying the importance of observation. Aristotle disagreed, locating the Forms in the natural world as the fundamental principles to be discovered through the inquiry into nature ( Metaphysics Z , in Barnes 1984).

Aristotle is recognized as giving the earliest systematic treatise on the nature of scientific inquiry in the western tradition, one which embraced observation and reasoning about the natural world. In the Prior and Posterior Analytics , Aristotle reflects first on the aims and then the methods of inquiry into nature. A number of features can be found which are still considered by most to be essential to science. For Aristotle, empiricism, careful observation (but passive observation, not controlled experiment), is the starting point. The aim is not merely recording of facts, though. For Aristotle, science ( epistêmê ) is a body of properly arranged knowledge or learning—the empirical facts, but also their ordering and display are of crucial importance. The aims of discovery, ordering, and display of facts partly determine the methods required of successful scientific inquiry. Also determinant is the nature of the knowledge being sought, and the explanatory causes proper to that kind of knowledge (see the discussion of the four causes in the entry on Aristotle on causality ).

In addition to careful observation, then, scientific method requires a logic as a system of reasoning for properly arranging, but also inferring beyond, what is known by observation. Methods of reasoning may include induction, prediction, or analogy, among others. Aristotle’s system (along with his catalogue of fallacious reasoning) was collected under the title the Organon . This title would be echoed in later works on scientific reasoning, such as Novum Organon by Francis Bacon, and Novum Organon Restorum by William Whewell (see below). In Aristotle’s Organon reasoning is divided primarily into two forms, a rough division which persists into modern times. The division, known most commonly today as deductive versus inductive method, appears in other eras and methodologies as analysis/​synthesis, non-ampliative/​ampliative, or even confirmation/​verification. The basic idea is there are two “directions” to proceed in our methods of inquiry: one away from what is observed, to the more fundamental, general, and encompassing principles; the other, from the fundamental and general to instances or implications of principles.

The basic aim and method of inquiry identified here can be seen as a theme running throughout the next two millennia of reflection on the correct way to seek after knowledge: carefully observe nature and then seek rules or principles which explain or predict its operation. The Aristotelian corpus provided the framework for a commentary tradition on scientific method independent of science itself (cosmos versus physics.) During the medieval period, figures such as Albertus Magnus (1206–1280), Thomas Aquinas (1225–1274), Robert Grosseteste (1175–1253), Roger Bacon (1214/1220–1292), William of Ockham (1287–1347), Andreas Vesalius (1514–1546), Giacomo Zabarella (1533–1589) all worked to clarify the kind of knowledge obtainable by observation and induction, the source of justification of induction, and best rules for its application. [ 2 ] Many of their contributions we now think of as essential to science (see also Laudan 1968). As Aristotle and Plato had employed a framework of reasoning either “to the forms” or “away from the forms”, medieval thinkers employed directions away from the phenomena or back to the phenomena. In analysis, a phenomena was examined to discover its basic explanatory principles; in synthesis, explanations of a phenomena were constructed from first principles.

During the Scientific Revolution these various strands of argument, experiment, and reason were forged into a dominant epistemic authority. The 16 th –18 th centuries were a period of not only dramatic advance in knowledge about the operation of the natural world—advances in mechanical, medical, biological, political, economic explanations—but also of self-awareness of the revolutionary changes taking place, and intense reflection on the source and legitimation of the method by which the advances were made. The struggle to establish the new authority included methodological moves. The Book of Nature, according to the metaphor of Galileo Galilei (1564–1642) or Francis Bacon (1561–1626), was written in the language of mathematics, of geometry and number. This motivated an emphasis on mathematical description and mechanical explanation as important aspects of scientific method. Through figures such as Henry More and Ralph Cudworth, a neo-Platonic emphasis on the importance of metaphysical reflection on nature behind appearances, particularly regarding the spiritual as a complement to the purely mechanical, remained an important methodological thread of the Scientific Revolution (see the entries on Cambridge platonists ; Boyle ; Henry More ; Galileo ).

In Novum Organum (1620), Bacon was critical of the Aristotelian method for leaping from particulars to universals too quickly. The syllogistic form of reasoning readily mixed those two types of propositions. Bacon aimed at the invention of new arts, principles, and directions. His method would be grounded in methodical collection of observations, coupled with correction of our senses (and particularly, directions for the avoidance of the Idols, as he called them, kinds of systematic errors to which naïve observers are prone.) The community of scientists could then climb, by a careful, gradual and unbroken ascent, to reliable general claims.

Bacon’s method has been criticized as impractical and too inflexible for the practicing scientist. Whewell would later criticize Bacon in his System of Logic for paying too little attention to the practices of scientists. It is hard to find convincing examples of Bacon’s method being put in to practice in the history of science, but there are a few who have been held up as real examples of 16 th century scientific, inductive method, even if not in the rigid Baconian mold: figures such as Robert Boyle (1627–1691) and William Harvey (1578–1657) (see the entry on Bacon ).

It is to Isaac Newton (1642–1727), however, that historians of science and methodologists have paid greatest attention. Given the enormous success of his Principia Mathematica and Opticks , this is understandable. The study of Newton’s method has had two main thrusts: the implicit method of the experiments and reasoning presented in the Opticks, and the explicit methodological rules given as the Rules for Philosophising (the Regulae) in Book III of the Principia . [ 3 ] Newton’s law of gravitation, the linchpin of his new cosmology, broke with explanatory conventions of natural philosophy, first for apparently proposing action at a distance, but more generally for not providing “true”, physical causes. The argument for his System of the World ( Principia , Book III) was based on phenomena, not reasoned first principles. This was viewed (mainly on the continent) as insufficient for proper natural philosophy. The Regulae counter this objection, re-defining the aims of natural philosophy by re-defining the method natural philosophers should follow. (See the entry on Newton’s philosophy .)

To his list of methodological prescriptions should be added Newton’s famous phrase “ hypotheses non fingo ” (commonly translated as “I frame no hypotheses”.) The scientist was not to invent systems but infer explanations from observations, as Bacon had advocated. This would come to be known as inductivism. In the century after Newton, significant clarifications of the Newtonian method were made. Colin Maclaurin (1698–1746), for instance, reconstructed the essential structure of the method as having complementary analysis and synthesis phases, one proceeding away from the phenomena in generalization, the other from the general propositions to derive explanations of new phenomena. Denis Diderot (1713–1784) and editors of the Encyclopédie did much to consolidate and popularize Newtonianism, as did Francesco Algarotti (1721–1764). The emphasis was often the same, as much on the character of the scientist as on their process, a character which is still commonly assumed. The scientist is humble in the face of nature, not beholden to dogma, obeys only his eyes, and follows the truth wherever it leads. It was certainly Voltaire (1694–1778) and du Chatelet (1706–1749) who were most influential in propagating the latter vision of the scientist and their craft, with Newton as hero. Scientific method became a revolutionary force of the Enlightenment. (See also the entries on Newton , Leibniz , Descartes , Boyle , Hume , enlightenment , as well as Shank 2008 for a historical overview.)

Not all 18 th century reflections on scientific method were so celebratory. Famous also are George Berkeley’s (1685–1753) attack on the mathematics of the new science, as well as the over-emphasis of Newtonians on observation; and David Hume’s (1711–1776) undermining of the warrant offered for scientific claims by inductive justification (see the entries on: George Berkeley ; David Hume ; Hume’s Newtonianism and Anti-Newtonianism ). Hume’s problem of induction motivated Immanuel Kant (1724–1804) to seek new foundations for empirical method, though as an epistemic reconstruction, not as any set of practical guidelines for scientists. Both Hume and Kant influenced the methodological reflections of the next century, such as the debate between Mill and Whewell over the certainty of inductive inferences in science.

The debate between John Stuart Mill (1806–1873) and William Whewell (1794–1866) has become the canonical methodological debate of the 19 th century. Although often characterized as a debate between inductivism and hypothetico-deductivism, the role of the two methods on each side is actually more complex. On the hypothetico-deductive account, scientists work to come up with hypotheses from which true observational consequences can be deduced—hence, hypothetico-deductive. Because Whewell emphasizes both hypotheses and deduction in his account of method, he can be seen as a convenient foil to the inductivism of Mill. However, equally if not more important to Whewell’s portrayal of scientific method is what he calls the “fundamental antithesis”. Knowledge is a product of the objective (what we see in the world around us) and subjective (the contributions of our mind to how we perceive and understand what we experience, which he called the Fundamental Ideas). Both elements are essential according to Whewell, and he was therefore critical of Kant for too much focus on the subjective, and John Locke (1632–1704) and Mill for too much focus on the senses. Whewell’s fundamental ideas can be discipline relative. An idea can be fundamental even if it is necessary for knowledge only within a given scientific discipline (e.g., chemical affinity for chemistry). This distinguishes fundamental ideas from the forms and categories of intuition of Kant. (See the entry on Whewell .)

Clarifying fundamental ideas would therefore be an essential part of scientific method and scientific progress. Whewell called this process “Discoverer’s Induction”. It was induction, following Bacon or Newton, but Whewell sought to revive Bacon’s account by emphasising the role of ideas in the clear and careful formulation of inductive hypotheses. Whewell’s induction is not merely the collecting of objective facts. The subjective plays a role through what Whewell calls the Colligation of Facts, a creative act of the scientist, the invention of a theory. A theory is then confirmed by testing, where more facts are brought under the theory, called the Consilience of Inductions. Whewell felt that this was the method by which the true laws of nature could be discovered: clarification of fundamental concepts, clever invention of explanations, and careful testing. Mill, in his critique of Whewell, and others who have cast Whewell as a fore-runner of the hypothetico-deductivist view, seem to have under-estimated the importance of this discovery phase in Whewell’s understanding of method (Snyder 1997a,b, 1999). Down-playing the discovery phase would come to characterize methodology of the early 20 th century (see section 3 ).

Mill, in his System of Logic , put forward a narrower view of induction as the essence of scientific method. For Mill, induction is the search first for regularities among events. Among those regularities, some will continue to hold for further observations, eventually gaining the status of laws. One can also look for regularities among the laws discovered in a domain, i.e., for a law of laws. Which “law law” will hold is time and discipline dependent and open to revision. One example is the Law of Universal Causation, and Mill put forward specific methods for identifying causes—now commonly known as Mill’s methods. These five methods look for circumstances which are common among the phenomena of interest, those which are absent when the phenomena are, or those for which both vary together. Mill’s methods are still seen as capturing basic intuitions about experimental methods for finding the relevant explanatory factors ( System of Logic (1843), see Mill entry). The methods advocated by Whewell and Mill, in the end, look similar. Both involve inductive generalization to covering laws. They differ dramatically, however, with respect to the necessity of the knowledge arrived at; that is, at the meta-methodological level (see the entries on Whewell and Mill entries).

3. Logic of method and critical responses

The quantum and relativistic revolutions in physics in the early 20 th century had a profound effect on methodology. Conceptual foundations of both theories were taken to show the defeasibility of even the most seemingly secure intuitions about space, time and bodies. Certainty of knowledge about the natural world was therefore recognized as unattainable. Instead a renewed empiricism was sought which rendered science fallible but still rationally justifiable.

Analyses of the reasoning of scientists emerged, according to which the aspects of scientific method which were of primary importance were the means of testing and confirming of theories. A distinction in methodology was made between the contexts of discovery and justification. The distinction could be used as a wedge between the particularities of where and how theories or hypotheses are arrived at, on the one hand, and the underlying reasoning scientists use (whether or not they are aware of it) when assessing theories and judging their adequacy on the basis of the available evidence. By and large, for most of the 20 th century, philosophy of science focused on the second context, although philosophers differed on whether to focus on confirmation or refutation as well as on the many details of how confirmation or refutation could or could not be brought about. By the mid-20 th century these attempts at defining the method of justification and the context distinction itself came under pressure. During the same period, philosophy of science developed rapidly, and from section 4 this entry will therefore shift from a primarily historical treatment of the scientific method towards a primarily thematic one.

Advances in logic and probability held out promise of the possibility of elaborate reconstructions of scientific theories and empirical method, the best example being Rudolf Carnap’s The Logical Structure of the World (1928). Carnap attempted to show that a scientific theory could be reconstructed as a formal axiomatic system—that is, a logic. That system could refer to the world because some of its basic sentences could be interpreted as observations or operations which one could perform to test them. The rest of the theoretical system, including sentences using theoretical or unobservable terms (like electron or force) would then either be meaningful because they could be reduced to observations, or they had purely logical meanings (called analytic, like mathematical identities). This has been referred to as the verifiability criterion of meaning. According to the criterion, any statement not either analytic or verifiable was strictly meaningless. Although the view was endorsed by Carnap in 1928, he would later come to see it as too restrictive (Carnap 1956). Another familiar version of this idea is operationalism of Percy William Bridgman. In The Logic of Modern Physics (1927) Bridgman asserted that every physical concept could be defined in terms of the operations one would perform to verify the application of that concept. Making good on the operationalisation of a concept even as simple as length, however, can easily become enormously complex (for measuring very small lengths, for instance) or impractical (measuring large distances like light years.)

Carl Hempel’s (1950, 1951) criticisms of the verifiability criterion of meaning had enormous influence. He pointed out that universal generalizations, such as most scientific laws, were not strictly meaningful on the criterion. Verifiability and operationalism both seemed too restrictive to capture standard scientific aims and practice. The tenuous connection between these reconstructions and actual scientific practice was criticized in another way. In both approaches, scientific methods are instead recast in methodological roles. Measurements, for example, were looked to as ways of giving meanings to terms. The aim of the philosopher of science was not to understand the methods per se , but to use them to reconstruct theories, their meanings, and their relation to the world. When scientists perform these operations, however, they will not report that they are doing them to give meaning to terms in a formal axiomatic system. This disconnect between methodology and the details of actual scientific practice would seem to violate the empiricism the Logical Positivists and Bridgman were committed to. The view that methodology should correspond to practice (to some extent) has been called historicism, or intuitionism. We turn to these criticisms and responses in section 3.4 . [ 4 ]

Positivism also had to contend with the recognition that a purely inductivist approach, along the lines of Bacon-Newton-Mill, was untenable. There was no pure observation, for starters. All observation was theory laden. Theory is required to make any observation, therefore not all theory can be derived from observation alone. (See the entry on theory and observation in science .) Even granting an observational basis, Hume had already pointed out that one could not deductively justify inductive conclusions without begging the question by presuming the success of the inductive method. Likewise, positivist attempts at analyzing how a generalization can be confirmed by observations of its instances were subject to a number of criticisms. Goodman (1965) and Hempel (1965) both point to paradoxes inherent in standard accounts of confirmation. Recent attempts at explaining how observations can serve to confirm a scientific theory are discussed in section 4 below.

The standard starting point for a non-inductive analysis of the logic of confirmation is known as the Hypothetico-Deductive (H-D) method. In its simplest form, a sentence of a theory which expresses some hypothesis is confirmed by its true consequences. As noted in section 2 , this method had been advanced by Whewell in the 19 th century, as well as Nicod (1924) and others in the 20 th century. Often, Hempel’s (1966) description of the H-D method, illustrated by the case of Semmelweiss’ inferential procedures in establishing the cause of childbed fever, has been presented as a key account of H-D as well as a foil for criticism of the H-D account of confirmation (see, for example, Lipton’s (2004) discussion of inference to the best explanation; also the entry on confirmation ). Hempel described Semmelsweiss’ procedure as examining various hypotheses explaining the cause of childbed fever. Some hypotheses conflicted with observable facts and could be rejected as false immediately. Others needed to be tested experimentally by deducing which observable events should follow if the hypothesis were true (what Hempel called the test implications of the hypothesis), then conducting an experiment and observing whether or not the test implications occurred. If the experiment showed the test implication to be false, the hypothesis could be rejected. If the experiment showed the test implications to be true, however, this did not prove the hypothesis true. The confirmation of a test implication does not verify a hypothesis, though Hempel did allow that “it provides at least some support, some corroboration or confirmation for it” (Hempel 1966: 8). The degree of this support then depends on the quantity, variety and precision of the supporting evidence.

Another approach that took off from the difficulties with inductive inference was Karl Popper’s critical rationalism or falsificationism (Popper 1959, 1963). Falsification is deductive and similar to H-D in that it involves scientists deducing observational consequences from the hypothesis under test. For Popper, however, the important point was not the degree of confirmation that successful prediction offered to a hypothesis. The crucial thing was the logical asymmetry between confirmation, based on inductive inference, and falsification, which can be based on a deductive inference. (This simple opposition was later questioned, by Lakatos, among others. See the entry on historicist theories of scientific rationality. )

Popper stressed that, regardless of the amount of confirming evidence, we can never be certain that a hypothesis is true without committing the fallacy of affirming the consequent. Instead, Popper introduced the notion of corroboration as a measure for how well a theory or hypothesis has survived previous testing—but without implying that this is also a measure for the probability that it is true.

Popper was also motivated by his doubts about the scientific status of theories like the Marxist theory of history or psycho-analysis, and so wanted to demarcate between science and pseudo-science. Popper saw this as an importantly different distinction than demarcating science from metaphysics. The latter demarcation was the primary concern of many logical empiricists. Popper used the idea of falsification to draw a line instead between pseudo and proper science. Science was science because its method involved subjecting theories to rigorous tests which offered a high probability of failing and thus refuting the theory.

A commitment to the risk of failure was important. Avoiding falsification could be done all too easily. If a consequence of a theory is inconsistent with observations, an exception can be added by introducing auxiliary hypotheses designed explicitly to save the theory, so-called ad hoc modifications. This Popper saw done in pseudo-science where ad hoc theories appeared capable of explaining anything in their field of application. In contrast, science is risky. If observations showed the predictions from a theory to be wrong, the theory would be refuted. Hence, scientific hypotheses must be falsifiable. Not only must there exist some possible observation statement which could falsify the hypothesis or theory, were it observed, (Popper called these the hypothesis’ potential falsifiers) it is crucial to the Popperian scientific method that such falsifications be sincerely attempted on a regular basis.

The more potential falsifiers of a hypothesis, the more falsifiable it would be, and the more the hypothesis claimed. Conversely, hypotheses without falsifiers claimed very little or nothing at all. Originally, Popper thought that this meant the introduction of ad hoc hypotheses only to save a theory should not be countenanced as good scientific method. These would undermine the falsifiabililty of a theory. However, Popper later came to recognize that the introduction of modifications (immunizations, he called them) was often an important part of scientific development. Responding to surprising or apparently falsifying observations often generated important new scientific insights. Popper’s own example was the observed motion of Uranus which originally did not agree with Newtonian predictions. The ad hoc hypothesis of an outer planet explained the disagreement and led to further falsifiable predictions. Popper sought to reconcile the view by blurring the distinction between falsifiable and not falsifiable, and speaking instead of degrees of testability (Popper 1985: 41f.).

From the 1960s on, sustained meta-methodological criticism emerged that drove philosophical focus away from scientific method. A brief look at those criticisms follows, with recommendations for further reading at the end of the entry.

Thomas Kuhn’s The Structure of Scientific Revolutions (1962) begins with a well-known shot across the bow for philosophers of science:

History, if viewed as a repository for more than anecdote or chronology, could produce a decisive transformation in the image of science by which we are now possessed. (1962: 1)

The image Kuhn thought needed transforming was the a-historical, rational reconstruction sought by many of the Logical Positivists, though Carnap and other positivists were actually quite sympathetic to Kuhn’s views. (See the entry on the Vienna Circle .) Kuhn shares with other of his contemporaries, such as Feyerabend and Lakatos, a commitment to a more empirical approach to philosophy of science. Namely, the history of science provides important data, and necessary checks, for philosophy of science, including any theory of scientific method.

The history of science reveals, according to Kuhn, that scientific development occurs in alternating phases. During normal science, the members of the scientific community adhere to the paradigm in place. Their commitment to the paradigm means a commitment to the puzzles to be solved and the acceptable ways of solving them. Confidence in the paradigm remains so long as steady progress is made in solving the shared puzzles. Method in this normal phase operates within a disciplinary matrix (Kuhn’s later concept of a paradigm) which includes standards for problem solving, and defines the range of problems to which the method should be applied. An important part of a disciplinary matrix is the set of values which provide the norms and aims for scientific method. The main values that Kuhn identifies are prediction, problem solving, simplicity, consistency, and plausibility.

An important by-product of normal science is the accumulation of puzzles which cannot be solved with resources of the current paradigm. Once accumulation of these anomalies has reached some critical mass, it can trigger a communal shift to a new paradigm and a new phase of normal science. Importantly, the values that provide the norms and aims for scientific method may have transformed in the meantime. Method may therefore be relative to discipline, time or place

Feyerabend also identified the aims of science as progress, but argued that any methodological prescription would only stifle that progress (Feyerabend 1988). His arguments are grounded in re-examining accepted “myths” about the history of science. Heroes of science, like Galileo, are shown to be just as reliant on rhetoric and persuasion as they are on reason and demonstration. Others, like Aristotle, are shown to be far more reasonable and far-reaching in their outlooks then they are given credit for. As a consequence, the only rule that could provide what he took to be sufficient freedom was the vacuous “anything goes”. More generally, even the methodological restriction that science is the best way to pursue knowledge, and to increase knowledge, is too restrictive. Feyerabend suggested instead that science might, in fact, be a threat to a free society, because it and its myth had become so dominant (Feyerabend 1978).

An even more fundamental kind of criticism was offered by several sociologists of science from the 1970s onwards who rejected the methodology of providing philosophical accounts for the rational development of science and sociological accounts of the irrational mistakes. Instead, they adhered to a symmetry thesis on which any causal explanation of how scientific knowledge is established needs to be symmetrical in explaining truth and falsity, rationality and irrationality, success and mistakes, by the same causal factors (see, e.g., Barnes and Bloor 1982, Bloor 1991). Movements in the Sociology of Science, like the Strong Programme, or in the social dimensions and causes of knowledge more generally led to extended and close examination of detailed case studies in contemporary science and its history. (See the entries on the social dimensions of scientific knowledge and social epistemology .) Well-known examinations by Latour and Woolgar (1979/1986), Knorr-Cetina (1981), Pickering (1984), Shapin and Schaffer (1985) seem to bear out that it was social ideologies (on a macro-scale) or individual interactions and circumstances (on a micro-scale) which were the primary causal factors in determining which beliefs gained the status of scientific knowledge. As they saw it therefore, explanatory appeals to scientific method were not empirically grounded.

A late, and largely unexpected, criticism of scientific method came from within science itself. Beginning in the early 2000s, a number of scientists attempting to replicate the results of published experiments could not do so. There may be close conceptual connection between reproducibility and method. For example, if reproducibility means that the same scientific methods ought to produce the same result, and all scientific results ought to be reproducible, then whatever it takes to reproduce a scientific result ought to be called scientific method. Space limits us to the observation that, insofar as reproducibility is a desired outcome of proper scientific method, it is not strictly a part of scientific method. (See the entry on reproducibility of scientific results .)

By the close of the 20 th century the search for the scientific method was flagging. Nola and Sankey (2000b) could introduce their volume on method by remarking that “For some, the whole idea of a theory of scientific method is yester-year’s debate …”.

Despite the many difficulties that philosophers encountered in trying to providing a clear methodology of conformation (or refutation), still important progress has been made on understanding how observation can provide evidence for a given theory. Work in statistics has been crucial for understanding how theories can be tested empirically, and in recent decades a huge literature has developed that attempts to recast confirmation in Bayesian terms. Here these developments can be covered only briefly, and we refer to the entry on confirmation for further details and references.

Statistics has come to play an increasingly important role in the methodology of the experimental sciences from the 19 th century onwards. At that time, statistics and probability theory took on a methodological role as an analysis of inductive inference, and attempts to ground the rationality of induction in the axioms of probability theory have continued throughout the 20 th century and in to the present. Developments in the theory of statistics itself, meanwhile, have had a direct and immense influence on the experimental method, including methods for measuring the uncertainty of observations such as the Method of Least Squares developed by Legendre and Gauss in the early 19 th century, criteria for the rejection of outliers proposed by Peirce by the mid-19 th century, and the significance tests developed by Gosset (a.k.a. “Student”), Fisher, Neyman & Pearson and others in the 1920s and 1930s (see, e.g., Swijtink 1987 for a brief historical overview; and also the entry on C.S. Peirce ).

These developments within statistics then in turn led to a reflective discussion among both statisticians and philosophers of science on how to perceive the process of hypothesis testing: whether it was a rigorous statistical inference that could provide a numerical expression of the degree of confidence in the tested hypothesis, or if it should be seen as a decision between different courses of actions that also involved a value component. This led to a major controversy among Fisher on the one side and Neyman and Pearson on the other (see especially Fisher 1955, Neyman 1956 and Pearson 1955, and for analyses of the controversy, e.g., Howie 2002, Marks 2000, Lenhard 2006). On Fisher’s view, hypothesis testing was a methodology for when to accept or reject a statistical hypothesis, namely that a hypothesis should be rejected by evidence if this evidence would be unlikely relative to other possible outcomes, given the hypothesis were true. In contrast, on Neyman and Pearson’s view, the consequence of error also had to play a role when deciding between hypotheses. Introducing the distinction between the error of rejecting a true hypothesis (type I error) and accepting a false hypothesis (type II error), they argued that it depends on the consequences of the error to decide whether it is more important to avoid rejecting a true hypothesis or accepting a false one. Hence, Fisher aimed for a theory of inductive inference that enabled a numerical expression of confidence in a hypothesis. To him, the important point was the search for truth, not utility. In contrast, the Neyman-Pearson approach provided a strategy of inductive behaviour for deciding between different courses of action. Here, the important point was not whether a hypothesis was true, but whether one should act as if it was.

Similar discussions are found in the philosophical literature. On the one side, Churchman (1948) and Rudner (1953) argued that because scientific hypotheses can never be completely verified, a complete analysis of the methods of scientific inference includes ethical judgments in which the scientists must decide whether the evidence is sufficiently strong or that the probability is sufficiently high to warrant the acceptance of the hypothesis, which again will depend on the importance of making a mistake in accepting or rejecting the hypothesis. Others, such as Jeffrey (1956) and Levi (1960) disagreed and instead defended a value-neutral view of science on which scientists should bracket their attitudes, preferences, temperament, and values when assessing the correctness of their inferences. For more details on this value-free ideal in the philosophy of science and its historical development, see Douglas (2009) and Howard (2003). For a broad set of case studies examining the role of values in science, see e.g. Elliott & Richards 2017.

In recent decades, philosophical discussions of the evaluation of probabilistic hypotheses by statistical inference have largely focused on Bayesianism that understands probability as a measure of a person’s degree of belief in an event, given the available information, and frequentism that instead understands probability as a long-run frequency of a repeatable event. Hence, for Bayesians probabilities refer to a state of knowledge, whereas for frequentists probabilities refer to frequencies of events (see, e.g., Sober 2008, chapter 1 for a detailed introduction to Bayesianism and frequentism as well as to likelihoodism). Bayesianism aims at providing a quantifiable, algorithmic representation of belief revision, where belief revision is a function of prior beliefs (i.e., background knowledge) and incoming evidence. Bayesianism employs a rule based on Bayes’ theorem, a theorem of the probability calculus which relates conditional probabilities. The probability that a particular hypothesis is true is interpreted as a degree of belief, or credence, of the scientist. There will also be a probability and a degree of belief that a hypothesis will be true conditional on a piece of evidence (an observation, say) being true. Bayesianism proscribes that it is rational for the scientist to update their belief in the hypothesis to that conditional probability should it turn out that the evidence is, in fact, observed (see, e.g., Sprenger & Hartmann 2019 for a comprehensive treatment of Bayesian philosophy of science). Originating in the work of Neyman and Person, frequentism aims at providing the tools for reducing long-run error rates, such as the error-statistical approach developed by Mayo (1996) that focuses on how experimenters can avoid both type I and type II errors by building up a repertoire of procedures that detect errors if and only if they are present. Both Bayesianism and frequentism have developed over time, they are interpreted in different ways by its various proponents, and their relations to previous criticism to attempts at defining scientific method are seen differently by proponents and critics. The literature, surveys, reviews and criticism in this area are vast and the reader is referred to the entries on Bayesian epistemology and confirmation .

5. Method in Practice

Attention to scientific practice, as we have seen, is not itself new. However, the turn to practice in the philosophy of science of late can be seen as a correction to the pessimism with respect to method in philosophy of science in later parts of the 20 th century, and as an attempted reconciliation between sociological and rationalist explanations of scientific knowledge. Much of this work sees method as detailed and context specific problem-solving procedures, and methodological analyses to be at the same time descriptive, critical and advisory (see Nickles 1987 for an exposition of this view). The following section contains a survey of some of the practice focuses. In this section we turn fully to topics rather than chronology.

A problem with the distinction between the contexts of discovery and justification that figured so prominently in philosophy of science in the first half of the 20 th century (see section 2 ) is that no such distinction can be clearly seen in scientific activity (see Arabatzis 2006). Thus, in recent decades, it has been recognized that study of conceptual innovation and change should not be confined to psychology and sociology of science, but are also important aspects of scientific practice which philosophy of science should address (see also the entry on scientific discovery ). Looking for the practices that drive conceptual innovation has led philosophers to examine both the reasoning practices of scientists and the wide realm of experimental practices that are not directed narrowly at testing hypotheses, that is, exploratory experimentation.

Examining the reasoning practices of historical and contemporary scientists, Nersessian (2008) has argued that new scientific concepts are constructed as solutions to specific problems by systematic reasoning, and that of analogy, visual representation and thought-experimentation are among the important reasoning practices employed. These ubiquitous forms of reasoning are reliable—but also fallible—methods of conceptual development and change. On her account, model-based reasoning consists of cycles of construction, simulation, evaluation and adaption of models that serve as interim interpretations of the target problem to be solved. Often, this process will lead to modifications or extensions, and a new cycle of simulation and evaluation. However, Nersessian also emphasizes that

creative model-based reasoning cannot be applied as a simple recipe, is not always productive of solutions, and even its most exemplary usages can lead to incorrect solutions. (Nersessian 2008: 11)

Thus, while on the one hand she agrees with many previous philosophers that there is no logic of discovery, discoveries can derive from reasoned processes, such that a large and integral part of scientific practice is

the creation of concepts through which to comprehend, structure, and communicate about physical phenomena …. (Nersessian 1987: 11)

Similarly, work on heuristics for discovery and theory construction by scholars such as Darden (1991) and Bechtel & Richardson (1993) present science as problem solving and investigate scientific problem solving as a special case of problem-solving in general. Drawing largely on cases from the biological sciences, much of their focus has been on reasoning strategies for the generation, evaluation, and revision of mechanistic explanations of complex systems.

Addressing another aspect of the context distinction, namely the traditional view that the primary role of experiments is to test theoretical hypotheses according to the H-D model, other philosophers of science have argued for additional roles that experiments can play. The notion of exploratory experimentation was introduced to describe experiments driven by the desire to obtain empirical regularities and to develop concepts and classifications in which these regularities can be described (Steinle 1997, 2002; Burian 1997; Waters 2007)). However the difference between theory driven experimentation and exploratory experimentation should not be seen as a sharp distinction. Theory driven experiments are not always directed at testing hypothesis, but may also be directed at various kinds of fact-gathering, such as determining numerical parameters. Vice versa , exploratory experiments are usually informed by theory in various ways and are therefore not theory-free. Instead, in exploratory experiments phenomena are investigated without first limiting the possible outcomes of the experiment on the basis of extant theory about the phenomena.

The development of high throughput instrumentation in molecular biology and neighbouring fields has given rise to a special type of exploratory experimentation that collects and analyses very large amounts of data, and these new ‘omics’ disciplines are often said to represent a break with the ideal of hypothesis-driven science (Burian 2007; Elliott 2007; Waters 2007; O’Malley 2007) and instead described as data-driven research (Leonelli 2012; Strasser 2012) or as a special kind of “convenience experimentation” in which many experiments are done simply because they are extraordinarily convenient to perform (Krohs 2012).

5.2 Computer methods and ‘new ways’ of doing science

The field of omics just described is possible because of the ability of computers to process, in a reasonable amount of time, the huge quantities of data required. Computers allow for more elaborate experimentation (higher speed, better filtering, more variables, sophisticated coordination and control), but also, through modelling and simulations, might constitute a form of experimentation themselves. Here, too, we can pose a version of the general question of method versus practice: does the practice of using computers fundamentally change scientific method, or merely provide a more efficient means of implementing standard methods?

Because computers can be used to automate measurements, quantifications, calculations, and statistical analyses where, for practical reasons, these operations cannot be otherwise carried out, many of the steps involved in reaching a conclusion on the basis of an experiment are now made inside a “black box”, without the direct involvement or awareness of a human. This has epistemological implications, regarding what we can know, and how we can know it. To have confidence in the results, computer methods are therefore subjected to tests of verification and validation.

The distinction between verification and validation is easiest to characterize in the case of computer simulations. In a typical computer simulation scenario computers are used to numerically integrate differential equations for which no analytic solution is available. The equations are part of the model the scientist uses to represent a phenomenon or system under investigation. Verifying a computer simulation means checking that the equations of the model are being correctly approximated. Validating a simulation means checking that the equations of the model are adequate for the inferences one wants to make on the basis of that model.

A number of issues related to computer simulations have been raised. The identification of validity and verification as the testing methods has been criticized. Oreskes et al. (1994) raise concerns that “validiation”, because it suggests deductive inference, might lead to over-confidence in the results of simulations. The distinction itself is probably too clean, since actual practice in the testing of simulations mixes and moves back and forth between the two (Weissart 1997; Parker 2008a; Winsberg 2010). Computer simulations do seem to have a non-inductive character, given that the principles by which they operate are built in by the programmers, and any results of the simulation follow from those in-built principles in such a way that those results could, in principle, be deduced from the program code and its inputs. The status of simulations as experiments has therefore been examined (Kaufmann and Smarr 1993; Humphreys 1995; Hughes 1999; Norton and Suppe 2001). This literature considers the epistemology of these experiments: what we can learn by simulation, and also the kinds of justifications which can be given in applying that knowledge to the “real” world. (Mayo 1996; Parker 2008b). As pointed out, part of the advantage of computer simulation derives from the fact that huge numbers of calculations can be carried out without requiring direct observation by the experimenter/​simulator. At the same time, many of these calculations are approximations to the calculations which would be performed first-hand in an ideal situation. Both factors introduce uncertainties into the inferences drawn from what is observed in the simulation.

For many of the reasons described above, computer simulations do not seem to belong clearly to either the experimental or theoretical domain. Rather, they seem to crucially involve aspects of both. This has led some authors, such as Fox Keller (2003: 200) to argue that we ought to consider computer simulation a “qualitatively different way of doing science”. The literature in general tends to follow Kaufmann and Smarr (1993) in referring to computer simulation as a “third way” for scientific methodology (theoretical reasoning and experimental practice are the first two ways.). It should also be noted that the debates around these issues have tended to focus on the form of computer simulation typical in the physical sciences, where models are based on dynamical equations. Other forms of simulation might not have the same problems, or have problems of their own (see the entry on computer simulations in science ).

In recent years, the rapid development of machine learning techniques has prompted some scholars to suggest that the scientific method has become “obsolete” (Anderson 2008, Carrol and Goodstein 2009). This has resulted in an intense debate on the relative merit of data-driven and hypothesis-driven research (for samples, see e.g. Mazzocchi 2015 or Succi and Coveney 2018). For a detailed treatment of this topic, we refer to the entry scientific research and big data .

6. Discourse on scientific method

Despite philosophical disagreements, the idea of the scientific method still figures prominently in contemporary discourse on many different topics, both within science and in society at large. Often, reference to scientific method is used in ways that convey either the legend of a single, universal method characteristic of all science, or grants to a particular method or set of methods privilege as a special ‘gold standard’, often with reference to particular philosophers to vindicate the claims. Discourse on scientific method also typically arises when there is a need to distinguish between science and other activities, or for justifying the special status conveyed to science. In these areas, the philosophical attempts at identifying a set of methods characteristic for scientific endeavors are closely related to the philosophy of science’s classical problem of demarcation (see the entry on science and pseudo-science ) and to the philosophical analysis of the social dimension of scientific knowledge and the role of science in democratic society.

One of the settings in which the legend of a single, universal scientific method has been particularly strong is science education (see, e.g., Bauer 1992; McComas 1996; Wivagg & Allchin 2002). [ 5 ] Often, ‘the scientific method’ is presented in textbooks and educational web pages as a fixed four or five step procedure starting from observations and description of a phenomenon and progressing over formulation of a hypothesis which explains the phenomenon, designing and conducting experiments to test the hypothesis, analyzing the results, and ending with drawing a conclusion. Such references to a universal scientific method can be found in educational material at all levels of science education (Blachowicz 2009), and numerous studies have shown that the idea of a general and universal scientific method often form part of both students’ and teachers’ conception of science (see, e.g., Aikenhead 1987; Osborne et al. 2003). In response, it has been argued that science education need to focus more on teaching about the nature of science, although views have differed on whether this is best done through student-led investigations, contemporary cases, or historical cases (Allchin, Andersen & Nielsen 2014)

Although occasionally phrased with reference to the H-D method, important historical roots of the legend in science education of a single, universal scientific method are the American philosopher and psychologist Dewey’s account of inquiry in How We Think (1910) and the British mathematician Karl Pearson’s account of science in Grammar of Science (1892). On Dewey’s account, inquiry is divided into the five steps of

(i) a felt difficulty, (ii) its location and definition, (iii) suggestion of a possible solution, (iv) development by reasoning of the bearing of the suggestions, (v) further observation and experiment leading to its acceptance or rejection. (Dewey 1910: 72)

Similarly, on Pearson’s account, scientific investigations start with measurement of data and observation of their correction and sequence from which scientific laws can be discovered with the aid of creative imagination. These laws have to be subject to criticism, and their final acceptance will have equal validity for “all normally constituted minds”. Both Dewey’s and Pearson’s accounts should be seen as generalized abstractions of inquiry and not restricted to the realm of science—although both Dewey and Pearson referred to their respective accounts as ‘the scientific method’.

Occasionally, scientists make sweeping statements about a simple and distinct scientific method, as exemplified by Feynman’s simplified version of a conjectures and refutations method presented, for example, in the last of his 1964 Cornell Messenger lectures. [ 6 ] However, just as often scientists have come to the same conclusion as recent philosophy of science that there is not any unique, easily described scientific method. For example, the physicist and Nobel Laureate Weinberg described in the paper “The Methods of Science … And Those By Which We Live” (1995) how

The fact that the standards of scientific success shift with time does not only make the philosophy of science difficult; it also raises problems for the public understanding of science. We do not have a fixed scientific method to rally around and defend. (1995: 8)

Interview studies with scientists on their conception of method shows that scientists often find it hard to figure out whether available evidence confirms their hypothesis, and that there are no direct translations between general ideas about method and specific strategies to guide how research is conducted (Schickore & Hangel 2019, Hangel & Schickore 2017)

Reference to the scientific method has also often been used to argue for the scientific nature or special status of a particular activity. Philosophical positions that argue for a simple and unique scientific method as a criterion of demarcation, such as Popperian falsification, have often attracted practitioners who felt that they had a need to defend their domain of practice. For example, references to conjectures and refutation as the scientific method are abundant in much of the literature on complementary and alternative medicine (CAM)—alongside the competing position that CAM, as an alternative to conventional biomedicine, needs to develop its own methodology different from that of science.

Also within mainstream science, reference to the scientific method is used in arguments regarding the internal hierarchy of disciplines and domains. A frequently seen argument is that research based on the H-D method is superior to research based on induction from observations because in deductive inferences the conclusion follows necessarily from the premises. (See, e.g., Parascandola 1998 for an analysis of how this argument has been made to downgrade epidemiology compared to the laboratory sciences.) Similarly, based on an examination of the practices of major funding institutions such as the National Institutes of Health (NIH), the National Science Foundation (NSF) and the Biomedical Sciences Research Practices (BBSRC) in the UK, O’Malley et al. (2009) have argued that funding agencies seem to have a tendency to adhere to the view that the primary activity of science is to test hypotheses, while descriptive and exploratory research is seen as merely preparatory activities that are valuable only insofar as they fuel hypothesis-driven research.

In some areas of science, scholarly publications are structured in a way that may convey the impression of a neat and linear process of inquiry from stating a question, devising the methods by which to answer it, collecting the data, to drawing a conclusion from the analysis of data. For example, the codified format of publications in most biomedical journals known as the IMRAD format (Introduction, Method, Results, Analysis, Discussion) is explicitly described by the journal editors as “not an arbitrary publication format but rather a direct reflection of the process of scientific discovery” (see the so-called “Vancouver Recommendations”, ICMJE 2013: 11). However, scientific publications do not in general reflect the process by which the reported scientific results were produced. For example, under the provocative title “Is the scientific paper a fraud?”, Medawar argued that scientific papers generally misrepresent how the results have been produced (Medawar 1963/1996). Similar views have been advanced by philosophers, historians and sociologists of science (Gilbert 1976; Holmes 1987; Knorr-Cetina 1981; Schickore 2008; Suppe 1998) who have argued that scientists’ experimental practices are messy and often do not follow any recognizable pattern. Publications of research results, they argue, are retrospective reconstructions of these activities that often do not preserve the temporal order or the logic of these activities, but are instead often constructed in order to screen off potential criticism (see Schickore 2008 for a review of this work).

Philosophical positions on the scientific method have also made it into the court room, especially in the US where judges have drawn on philosophy of science in deciding when to confer special status to scientific expert testimony. A key case is Daubert vs Merrell Dow Pharmaceuticals (92–102, 509 U.S. 579, 1993). In this case, the Supreme Court argued in its 1993 ruling that trial judges must ensure that expert testimony is reliable, and that in doing this the court must look at the expert’s methodology to determine whether the proffered evidence is actually scientific knowledge. Further, referring to works of Popper and Hempel the court stated that

ordinarily, a key question to be answered in determining whether a theory or technique is scientific knowledge … is whether it can be (and has been) tested. (Justice Blackmun, Daubert v. Merrell Dow Pharmaceuticals; see Other Internet Resources for a link to the opinion)

But as argued by Haack (2005a,b, 2010) and by Foster & Hubner (1999), by equating the question of whether a piece of testimony is reliable with the question whether it is scientific as indicated by a special methodology, the court was producing an inconsistent mixture of Popper’s and Hempel’s philosophies, and this has later led to considerable confusion in subsequent case rulings that drew on the Daubert case (see Haack 2010 for a detailed exposition).

The difficulties around identifying the methods of science are also reflected in the difficulties of identifying scientific misconduct in the form of improper application of the method or methods of science. One of the first and most influential attempts at defining misconduct in science was the US definition from 1989 that defined misconduct as

fabrication, falsification, plagiarism, or other practices that seriously deviate from those that are commonly accepted within the scientific community . (Code of Federal Regulations, part 50, subpart A., August 8, 1989, italics added)

However, the “other practices that seriously deviate” clause was heavily criticized because it could be used to suppress creative or novel science. For example, the National Academy of Science stated in their report Responsible Science (1992) that it

wishes to discourage the possibility that a misconduct complaint could be lodged against scientists based solely on their use of novel or unorthodox research methods. (NAS: 27)

This clause was therefore later removed from the definition. For an entry into the key philosophical literature on conduct in science, see Shamoo & Resnick (2009).

The question of the source of the success of science has been at the core of philosophy since the beginning of modern science. If viewed as a matter of epistemology more generally, scientific method is a part of the entire history of philosophy. Over that time, science and whatever methods its practitioners may employ have changed dramatically. Today, many philosophers have taken up the banners of pluralism or of practice to focus on what are, in effect, fine-grained and contextually limited examinations of scientific method. Others hope to shift perspectives in order to provide a renewed general account of what characterizes the activity we call science.

One such perspective has been offered recently by Hoyningen-Huene (2008, 2013), who argues from the history of philosophy of science that after three lengthy phases of characterizing science by its method, we are now in a phase where the belief in the existence of a positive scientific method has eroded and what has been left to characterize science is only its fallibility. First was a phase from Plato and Aristotle up until the 17 th century where the specificity of scientific knowledge was seen in its absolute certainty established by proof from evident axioms; next was a phase up to the mid-19 th century in which the means to establish the certainty of scientific knowledge had been generalized to include inductive procedures as well. In the third phase, which lasted until the last decades of the 20 th century, it was recognized that empirical knowledge was fallible, but it was still granted a special status due to its distinctive mode of production. But now in the fourth phase, according to Hoyningen-Huene, historical and philosophical studies have shown how “scientific methods with the characteristics as posited in the second and third phase do not exist” (2008: 168) and there is no longer any consensus among philosophers and historians of science about the nature of science. For Hoyningen-Huene, this is too negative a stance, and he therefore urges the question about the nature of science anew. His own answer to this question is that “scientific knowledge differs from other kinds of knowledge, especially everyday knowledge, primarily by being more systematic” (Hoyningen-Huene 2013: 14). Systematicity can have several different dimensions: among them are more systematic descriptions, explanations, predictions, defense of knowledge claims, epistemic connectedness, ideal of completeness, knowledge generation, representation of knowledge and critical discourse. Hence, what characterizes science is the greater care in excluding possible alternative explanations, the more detailed elaboration with respect to data on which predictions are based, the greater care in detecting and eliminating sources of error, the more articulate connections to other pieces of knowledge, etc. On this position, what characterizes science is not that the methods employed are unique to science, but that the methods are more carefully employed.

Another, similar approach has been offered by Haack (2003). She sets off, similar to Hoyningen-Huene, from a dissatisfaction with the recent clash between what she calls Old Deferentialism and New Cynicism. The Old Deferentialist position is that science progressed inductively by accumulating true theories confirmed by empirical evidence or deductively by testing conjectures against basic statements; while the New Cynics position is that science has no epistemic authority and no uniquely rational method and is merely just politics. Haack insists that contrary to the views of the New Cynics, there are objective epistemic standards, and there is something epistemologically special about science, even though the Old Deferentialists pictured this in a wrong way. Instead, she offers a new Critical Commonsensist account on which standards of good, strong, supportive evidence and well-conducted, honest, thorough and imaginative inquiry are not exclusive to the sciences, but the standards by which we judge all inquirers. In this sense, science does not differ in kind from other kinds of inquiry, but it may differ in the degree to which it requires broad and detailed background knowledge and a familiarity with a technical vocabulary that only specialists may possess.

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How to cite this entry . Preview the PDF version of this entry at the Friends of the SEP Society . Look up topics and thinkers related to this entry at the Internet Philosophy Ontology Project (InPhO). Enhanced bibliography for this entry at PhilPapers , with links to its database.

• Blackmun opinion , in Daubert v. Merrell Dow Pharmaceuticals (92–102), 509 U.S. 579 (1993).
• Scientific Method at philpapers. Darrell Rowbottom (ed.).
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Definitions of terms in a bachelors', master's or PhD thesis - 3 cases

Finding a suitable definition for a term in a bachelor's thesis, master's thesis or dissertation is often tedious, but absolutely necessary. Otherwise, you start from scratch. There are often many definitions for the same term...

What definition do I use? Fortunately, there are proven methods for searching and formulating definitions. This will help you get a grip on the terms. Let's go!

What is a definition?

A definition always leads a term back to a generic term. In an academic paper, such as a Bachelor's thesis, Master's Thesis or dissertation, the definitions MUST come from recognized sources. But sometimes there aren’t any scientific sources for a research subject, which is especially true when exploring a new field. At that point, you have to formulate a definition yourself.

Three cases can be distinguished with regard to the definition of terms:

• Accepted term - Case 1
• New inconsistent concept - case 2
• New, largely unexplored term (YOUR focus) - Case 3.

Let's go through the cases in order.

Case 1: Definition of an accepted term

The term has been known for a long time and is frequently used in scientific sources. The definitions in different sources are relatively consistent. This can be seen from the fact that the same source references appear repeatedly in definitions.

Examples of such terms are attitudes, motivation, incentives, learning disabilities or controlling.

Such terms are hardly ever discussed anymore. They are simply implied by the definition. Nevertheless, there may be new variations of definitions. However, they are usually for a very specific term and therefore not relevant for your text.

A quick way to get started in defining these terms:

• Be sure to use the correct spelling of the term. Distinguish singular and plural. Search the term in Google.
• Go to Wikipedia and look up the references inside the term article. Focus on scientific sources like books or papers. (Of course you can also do this without a wiki!)
• Locate these sources and gather them. Search at the beginning of the chapter or book for possible definitions. Usually several authors are cited. This is followed by a proposal for a definition, as it is subsequently used in the textbook.
• Adopt this definition, but refer to the original source if it came from another source.
• Write the definition into your text, with the full reference.

IMPORTANT: Do not use Google, Wikipedia, other pure online sources or encyclopedias as a source reference for definitions of recognized terms. It signals carelessness, if not laziness... The only possible sources for the definition of terms are

• textbooks or reference books
• scientific articles (paper)
• lists of standards like DIN, ISO, Law Codices...

By the way, the best sources are standards like DIN and ISO or laws of all kinds. These legal definitions are the best.

Case 2: Definition of still inconsistent term

A characteristic of this type of term is the existence of several definitions by different authors. Ultimately, each definition focuses on specific characteristics. That is why it is often not "either-or", but "both-also".

This is reminiscent of the example with the elephant, which six blind people examine by touch and then describe. The person who touches the trunk says it is a snake. The one sitting on his back says, "That's a mountain." Whoever touches the legs says it is a tree trunk, the ears are ferns, the ivory teeth are field cliffs, etc.

This situation is typical for relatively new subject areas where there is still a lot to discover. New is of course relative and depends on the subject. If there are only five to ten articles on a subject area, this indicates a need for research.

Examples of such terms are social media, trust, mediation.

Proceed as follows when defining these terms for the dissertation:

• Search for the relevant authors on the subject area.
• Search in their scientific articles for the definitions used.
• Make an overview of these definitions. Literally and with reference!!
• Filter out the substance from the respective definitions, the central words and the generic term.
• Check which of these definitions fits your approach.
• Use the appropriate definition or combine several definitions.
• Reconsider and justify your decision. Further work depends on this.
• Ask experts in the field, authors of papers.
• Agree upon the definition with the supervisor of the dissertation.

Case 3: Definition of new, still largely unexplored terms = focus of a dissertation

In this case it is a completely new concept. So far, there are only definitions of experts with experience in the subject area. These have themselves formulated a definition, but it has not been recognized officially. In any case, there are no recognized scientific sources on the field of research to date. But you need a clear definition for your text.

IMPORTANT: Please think very carefully if you really want to work on this topic. The lack of scientifically formulated definitions suggests that this could be an extremely tedious project. You practically have to explore the field without any orientation in the literature. Maybe you are the first to build a model. It could be heroic, but I'm sure it's a lot of work.

This is how you should proceed with new terms in the dissertation:

• Collect all available publications with information on this topic.
• Sort the publications found according to their quality, substance and scientific quality. Use only the best sources (data sources must be traceable and trustworthy)
• Make a comprehensive word cloud of relevant terms and variants.
• Collect the characteristics for the object or terms.
• Think carefully about which other terms are related to the term.
• Filter the ideas and arguments from texts that describe characteristics and are heading towards a definition.
• Make a list of these attributes. These are candidates for the definition.
• Search for generic terms for the term in appropriate documents.
• Make a list.

If you have collected enough sources or five days have passed (whichever happens first):

• Formulate YOUR first definition.
• Leave it for a day or two.
• Check, revise, iterate, collect the evidence, share the definition with others.
• Formulate the working definition for your text. It may be refined along the way.
• Discuss the draft of your definition with the supervisor or even with experts as soon as you are sure you have something to show.

IMPORTANT: Include the reference for each quote.

Now formulate the preliminary working definition that you will use during your research for the dissertation. Refine it if necessary.

Good luck writing your text! Silvio and the Aristolo Team

PS: Check out the Thesis-ABC and the Thesis Guide for writing a bachelor's or master's thesis in 31 days.