hypothesis has a prediction

Hypotheses Versus Predictions

Hypotheses and predictions are not the same thing..

Posted January 12, 2018

Blogs are not typically places where professors post views about arcane matters. But blogs have the advantage of providing places to convey quick messages that may be of interest to selected parties. I've written this blog to point students and others to a spot where a useful distinction is made that, as far as I know, hasn't been made before. The distinction concerns two words that are used interchangeably though they shouldn't be. The words are hypothesis (or hypotheses) and prediction (or predictions).

It's not uncommon to see these words swapped for each other willy-nilly, as in, "We sought to test the hypothesis that the two groups in our study would remember the same number of words," or "We sought to test the prediction that the two groups in our study would remember the same number of words." Indifference to the contrast in meaning between "hypothesis" and "prediction" is unfortunate, in my view, because "hypothesis" and "prediction" (or "hypotheses" and "predictions") mean very different things. A student proposing an experiment, or an already-graduated researcher doing the same, will have more gravitas if s/he states a hypothesis from which a prediction follows than if s/he proclaims a prediction from thin air.

Consider the prediction that the time for two balls to drop from the Tower Pisa will be the same if the two balls have different mass. This is the famous prediction tested (or allegedly tested) by Galileo. This experiment — one of the first in the history of science — was designed to test two contrasting predictions. One was that the time for the two balls to drop would be the same. The other was that the time for the heavier ball to drop would be shorter. (The third possibility, that the lighter ball would drop more quickly, was logically possible but not taken seriously.) The importance of the predictions came from the hypotheses on which they were based. Those hypotheses couldn't have been more different. One stemmed from Aristotle and had an entire system of assumptions about the world's basic elements, including the idea that motion requires a driving force, with the force being greater for a heavier object than a lighter one, in which case the heavier object would land first. The other hypothesis came from an entirely different conception which made no such assumptions, as crystallized (later) by Newton. It led to the prediction of equivalent drop times. Dropping two balls and seeing which, if either, landed first was a more important experiment if it was motivated by different hypotheses than if it was motivated by two different off-the-cuff predictions. Predictions can be ticked off by a monkey at a typewriter, so to speak. Anyone can list possible outcomes. That's not good (interesting) science.

Let me say this, then, to students or colleagues reading this (some of whom might be people to whom I give the URL for this blog): Be cognizant of the distinction between "hypotheses" and "predictions." Hypotheses are claims or educated guesses about the world or the part of it you are studying. Predictions are derived from hypotheses and define opportunities for seeing whether expected consequences of hypotheses are observed. Critically, if a prediction is confirmed — if the data agree with the prediction — you can say that the data are consistent with the prediction and, from that point onward you can also say that the data are consistent with the hypothesis that spawned the prediction. You can't say that the data prove the hypothesis, however. The reason is that any of an infinite number of other hypotheses might have caused the outcome you obtained. If you say that a given data pattern proves that such-and-such hypothesis is correct, you will be shot down, and rightly so, for any given data pattern can be explained by an infinite number of possible hypotheses. It's fine to say that the data you have are consistent with a hypothesis, and it's fine for you to say that a hypothesis is (or appears to be) wrong because the data you got are inconsistent with it. The latter outcome is the culmination of the hypothetico-deductive method, where you can say that a hypothesis is, or seems to be, incorrect if you have data that violates it, but you can never say that a hypothesis is right because you have data consistent with it; some other hypothesis might actually correspond to the true explanation of what you found. By creating hypotheses that lead to different predictions, you can see which prediction is not supported, and insofar as you can make progress by rejecting hypotheses, you can depersonalize your science by developing hypotheses that are worth disproving. The worth of a hypothesis will be judged by how resistant it is to attempts at disconfirmation over many years by many investigators using many methods.

Some final comments.... First, hypotheses don't predict; people do. You can say that a prediction arose from a hypothesis, but you can't say, or shouldn't say, that a hypothesis predicts something.

Second, beware of the admonition that hypotheses are weak if they predict no differences. Newtonian mechanics predicts no difference in the landing times of heavy and light objects dropped from the same height at the same time. The fact that Newtonian mechanics predicts no difference hardly means that Newtonian mechanics is lightweight. Instead, the prediction of no difference in landing times demands creation of extremely sensitive experiments. Anyone can get no difference with sloppy experiments. By contrast, getting no difference when a sophisticated hypothesis predicts none and when one has gone to great lengths to detect even the tiniest possible difference ... now that's good science.

Third and finally, according to the hypothesis that a blog about hypotheses versus predictions will prove informative, the prediction that follows is that those who read and heed this blog will exhibit less confusion about which term to use when. More important, they will exhibit greater gravitas and deeper thoughtfulness as they generate their hypotheses and subsequent predictions. I hope this blog will prove useful. Its utility will be judged by how long it takes to disconfirm the prediction I have just advanced.

David A. Rosenbaum, Ph.D. , is a cognitive psychologist and a Distinguished Professor of Psychology at the University of California, Riverside.

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Course: biology library   >   unit 1, the scientific method.

• Controlled experiments
• The scientific method and experimental design

Introduction

• Make an observation.
• Ask a question.
• Form a hypothesis , or testable explanation.
• Make a prediction based on the hypothesis.
• Test the prediction.
• Iterate: use the results to make new hypotheses or predictions.

Scientific method example: Failure to toast

1. make an observation..

• Observation: the toaster won't toast.

2. Ask a question.

• Question: Why won't my toaster toast?

3. Propose a hypothesis.

• Hypothesis: Maybe the outlet is broken.

4. Make predictions.

• Prediction: If I plug the toaster into a different outlet, then it will toast the bread.

5. Test the predictions.

• Test of prediction: Plug the toaster into a different outlet and try again.
• If the toaster does toast, then the hypothesis is supported—likely correct.
• If the toaster doesn't toast, then the hypothesis is not supported—likely wrong.

Logical possibility

Practical possibility, building a body of evidence, 6. iterate..

• Iteration time!
• If the hypothesis was supported, we might do additional tests to confirm it, or revise it to be more specific. For instance, we might investigate why the outlet is broken.
• If the hypothesis was not supported, we would come up with a new hypothesis. For instance, the next hypothesis might be that there's a broken wire in the toaster.

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How to Write a Great Hypothesis

Hypothesis Definition, Format, Examples, and Tips

Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

Amy Morin, LCSW, is a psychotherapist and international bestselling author. Her books, including "13 Things Mentally Strong People Don't Do," have been translated into more than 40 languages. Her TEDx talk,  "The Secret of Becoming Mentally Strong," is one of the most viewed talks of all time.

Verywell / Alex Dos Diaz

• The Scientific Method

Hypothesis Format

Falsifiability of a hypothesis.

• Operationalization

Hypothesis Types

Hypotheses examples.

• Collecting Data

A hypothesis is a tentative statement about the relationship between two or more variables. It is a specific, testable prediction about what you expect to happen in a study. It is a preliminary answer to your question that helps guide the research process.

Consider a study designed to examine the relationship between sleep deprivation and test performance. The hypothesis might be: "This study is designed to assess the hypothesis that sleep-deprived people will perform worse on a test than individuals who are not sleep-deprived."

At a Glance

A hypothesis is crucial to scientific research because it offers a clear direction for what the researchers are looking to find. This allows them to design experiments to test their predictions and add to our scientific knowledge about the world. This article explores how a hypothesis is used in psychology research, how to write a good hypothesis, and the different types of hypotheses you might use.

The Hypothesis in the Scientific Method

In the scientific method , whether it involves research in psychology, biology, or some other area, a hypothesis represents what the researchers think will happen in an experiment. The scientific method involves the following steps:

• Forming a question
• Performing background research
• Creating a hypothesis
• Designing an experiment
• Collecting data
• Analyzing the results
• Drawing conclusions
• Communicating the results

The hypothesis is a prediction, but it involves more than a guess. Most of the time, the hypothesis begins with a question which is then explored through background research. At this point, researchers then begin to develop a testable hypothesis.

Unless you are creating an exploratory study, your hypothesis should always explain what you  expect  to happen.

In a study exploring the effects of a particular drug, the hypothesis might be that researchers expect the drug to have some type of effect on the symptoms of a specific illness. In psychology, the hypothesis might focus on how a certain aspect of the environment might influence a particular behavior.

Remember, a hypothesis does not have to be correct. While the hypothesis predicts what the researchers expect to see, the goal of the research is to determine whether this guess is right or wrong. When conducting an experiment, researchers might explore numerous factors to determine which ones might contribute to the ultimate outcome.

In many cases, researchers may find that the results of an experiment  do not  support the original hypothesis. When writing up these results, the researchers might suggest other options that should be explored in future studies.

In many cases, researchers might draw a hypothesis from a specific theory or build on previous research. For example, prior research has shown that stress can impact the immune system. So a researcher might hypothesize: "People with high-stress levels will be more likely to contract a common cold after being exposed to the virus than people who have low-stress levels."

In other instances, researchers might look at commonly held beliefs or folk wisdom. "Birds of a feather flock together" is one example of folk adage that a psychologist might try to investigate. The researcher might pose a specific hypothesis that "People tend to select romantic partners who are similar to them in interests and educational level."

Elements of a Good Hypothesis

So how do you write a good hypothesis? When trying to come up with a hypothesis for your research or experiments, ask yourself the following questions:

• Is your hypothesis based on your research on a topic?
• Can your hypothesis be tested?
• Does your hypothesis include independent and dependent variables?

Before you come up with a specific hypothesis, spend some time doing background research. Once you have completed a literature review, start thinking about potential questions you still have. Pay attention to the discussion section in the  journal articles you read . Many authors will suggest questions that still need to be explored.

How to Formulate a Good Hypothesis

To form a hypothesis, you should take these steps:

• Collect as many observations about a topic or problem as you can.
• Evaluate these observations and look for possible causes of the problem.
• Create a list of possible explanations that you might want to explore.
• After you have developed some possible hypotheses, think of ways that you could confirm or disprove each hypothesis through experimentation. This is known as falsifiability.

In the scientific method ,  falsifiability is an important part of any valid hypothesis. In order to test a claim scientifically, it must be possible that the claim could be proven false.

Students sometimes confuse the idea of falsifiability with the idea that it means that something is false, which is not the case. What falsifiability means is that  if  something was false, then it is possible to demonstrate that it is false.

One of the hallmarks of pseudoscience is that it makes claims that cannot be refuted or proven false.

The Importance of Operational Definitions

A variable is a factor or element that can be changed and manipulated in ways that are observable and measurable. However, the researcher must also define how the variable will be manipulated and measured in the study.

Operational definitions are specific definitions for all relevant factors in a study. This process helps make vague or ambiguous concepts detailed and measurable.

For example, a researcher might operationally define the variable " test anxiety " as the results of a self-report measure of anxiety experienced during an exam. A "study habits" variable might be defined by the amount of studying that actually occurs as measured by time.

These precise descriptions are important because many things can be measured in various ways. Clearly defining these variables and how they are measured helps ensure that other researchers can replicate your results.

Replicability

One of the basic principles of any type of scientific research is that the results must be replicable.

Replication means repeating an experiment in the same way to produce the same results. By clearly detailing the specifics of how the variables were measured and manipulated, other researchers can better understand the results and repeat the study if needed.

Some variables are more difficult than others to define. For example, how would you operationally define a variable such as aggression ? For obvious ethical reasons, researchers cannot create a situation in which a person behaves aggressively toward others.

To measure this variable, the researcher must devise a measurement that assesses aggressive behavior without harming others. The researcher might utilize a simulated task to measure aggressiveness in this situation.

Hypothesis Checklist

• Does your hypothesis focus on something that you can actually test?
• Does your hypothesis include both an independent and dependent variable?
• Can you manipulate the variables?
• Can your hypothesis be tested without violating ethical standards?

The hypothesis you use will depend on what you are investigating and hoping to find. Some of the main types of hypotheses that you might use include:

• Simple hypothesis : This type of hypothesis suggests there is a relationship between one independent variable and one dependent variable.
• Complex hypothesis : This type suggests a relationship between three or more variables, such as two independent and dependent variables.
• Null hypothesis : This hypothesis suggests no relationship exists between two or more variables.
• Alternative hypothesis : This hypothesis states the opposite of the null hypothesis.
• Statistical hypothesis : This hypothesis uses statistical analysis to evaluate a representative population sample and then generalizes the findings to the larger group.
• Logical hypothesis : This hypothesis assumes a relationship between variables without collecting data or evidence.

A hypothesis often follows a basic format of "If {this happens} then {this will happen}." One way to structure your hypothesis is to describe what will happen to the  dependent variable  if you change the  independent variable .

The basic format might be: "If {these changes are made to a certain independent variable}, then we will observe {a change in a specific dependent variable}."

A few examples of simple hypotheses:

• "Students who eat breakfast will perform better on a math exam than students who do not eat breakfast."
• "Students who experience test anxiety before an English exam will get lower scores than students who do not experience test anxiety."​
• "Motorists who talk on the phone while driving will be more likely to make errors on a driving course than those who do not talk on the phone."
• "Children who receive a new reading intervention will have higher reading scores than students who do not receive the intervention."

Examples of a complex hypothesis include:

• "People with high-sugar diets and sedentary activity levels are more likely to develop depression."
• "Younger people who are regularly exposed to green, outdoor areas have better subjective well-being than older adults who have limited exposure to green spaces."

Examples of a null hypothesis include:

• "There is no difference in anxiety levels between people who take St. John's wort supplements and those who do not."
• "There is no difference in scores on a memory recall task between children and adults."
• "There is no difference in aggression levels between children who play first-person shooter games and those who do not."

Examples of an alternative hypothesis:

• "People who take St. John's wort supplements will have less anxiety than those who do not."
• "Adults will perform better on a memory task than children."
• "Children who play first-person shooter games will show higher levels of aggression than children who do not." 

Collecting Data on Your Hypothesis

Once a researcher has formed a testable hypothesis, the next step is to select a research design and start collecting data. The research method depends largely on exactly what they are studying. There are two basic types of research methods: descriptive research and experimental research.

Descriptive Research Methods

Descriptive research such as  case studies ,  naturalistic observations , and surveys are often used when  conducting an experiment is difficult or impossible. These methods are best used to describe different aspects of a behavior or psychological phenomenon.

Once a researcher has collected data using descriptive methods, a  correlational study  can examine how the variables are related. This research method might be used to investigate a hypothesis that is difficult to test experimentally.

Experimental Research Methods

Experimental methods  are used to demonstrate causal relationships between variables. In an experiment, the researcher systematically manipulates a variable of interest (known as the independent variable) and measures the effect on another variable (known as the dependent variable).

Unlike correlational studies, which can only be used to determine if there is a relationship between two variables, experimental methods can be used to determine the actual nature of the relationship—whether changes in one variable actually  cause  another to change.

The hypothesis is a critical part of any scientific exploration. It represents what researchers expect to find in a study or experiment. In situations where the hypothesis is unsupported by the research, the research still has value. Such research helps us better understand how different aspects of the natural world relate to one another. It also helps us develop new hypotheses that can then be tested in the future.

Thompson WH, Skau S. On the scope of scientific hypotheses .  R Soc Open Sci . 2023;10(8):230607. doi:10.1098/rsos.230607

Taran S, Adhikari NKJ, Fan E. Falsifiability in medicine: what clinicians can learn from Karl Popper [published correction appears in Intensive Care Med. 2021 Jun 17;:].  Intensive Care Med . 2021;47(9):1054-1056. doi:10.1007/s00134-021-06432-z

Eyler AA. Research Methods for Public Health . 1st ed. Springer Publishing Company; 2020. doi:10.1891/9780826182067.0004

Nosek BA, Errington TM. What is replication ?  PLoS Biol . 2020;18(3):e3000691. doi:10.1371/journal.pbio.3000691

Aggarwal R, Ranganathan P. Study designs: Part 2 - Descriptive studies .  Perspect Clin Res . 2019;10(1):34-36. doi:10.4103/picr.PICR_154_18

Nevid J. Psychology: Concepts and Applications. Wadworth, 2013.

By Kendra Cherry, MSEd Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

What Is a Hypothesis? (Science)

If...,Then...

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A hypothesis (plural hypotheses) is a proposed explanation for an observation. The definition depends on the subject.

In science, a hypothesis is part of the scientific method. It is a prediction or explanation that is tested by an experiment. Observations and experiments may disprove a scientific hypothesis, but can never entirely prove one.

In the study of logic, a hypothesis is an if-then proposition, typically written in the form, "If X , then Y ."

In common usage, a hypothesis is simply a proposed explanation or prediction, which may or may not be tested.

Writing a Hypothesis

Most scientific hypotheses are proposed in the if-then format because it's easy to design an experiment to see whether or not a cause and effect relationship exists between the independent variable and the dependent variable . The hypothesis is written as a prediction of the outcome of the experiment.

• Null Hypothesis and Alternative Hypothesis

Statistically, it's easier to show there is no relationship between two variables than to support their connection. So, scientists often propose the null hypothesis . The null hypothesis assumes changing the independent variable will have no effect on the dependent variable.

In contrast, the alternative hypothesis suggests changing the independent variable will have an effect on the dependent variable. Designing an experiment to test this hypothesis can be trickier because there are many ways to state an alternative hypothesis.

For example, consider a possible relationship between getting a good night's sleep and getting good grades. The null hypothesis might be stated: "The number of hours of sleep students get is unrelated to their grades" or "There is no correlation between hours of sleep and grades."

An experiment to test this hypothesis might involve collecting data, recording average hours of sleep for each student and grades. If a student who gets eight hours of sleep generally does better than students who get four hours of sleep or 10 hours of sleep, the hypothesis might be rejected.

But the alternative hypothesis is harder to propose and test. The most general statement would be: "The amount of sleep students get affects their grades." The hypothesis might also be stated as "If you get more sleep, your grades will improve" or "Students who get nine hours of sleep have better grades than those who get more or less sleep."

In an experiment, you can collect the same data, but the statistical analysis is less likely to give you a high confidence limit.

Usually, a scientist starts out with the null hypothesis. From there, it may be possible to propose and test an alternative hypothesis, to narrow down the relationship between the variables.

Example of a Hypothesis

Examples of a hypothesis include:

• If you drop a rock and a feather, (then) they will fall at the same rate.
• Plants need sunlight in order to live. (if sunlight, then life)
• Eating sugar gives you energy. (if sugar, then energy)
• White, Jay D.  Research in Public Administration . Conn., 1998.
• Schick, Theodore, and Lewis Vaughn.  How to Think about Weird Things: Critical Thinking for a New Age . McGraw-Hill Higher Education, 2002.
• Null Hypothesis Definition and Examples
• Definition of a Hypothesis
• What Are the Elements of a Good Hypothesis?
• Six Steps of the Scientific Method
• Independent Variable Definition and Examples
• What Are Examples of a Hypothesis?
• Understanding Simple vs Controlled Experiments
• Scientific Method Flow Chart
• Scientific Method Vocabulary Terms
• What Is a Testable Hypothesis?
• Null Hypothesis Examples
• What 'Fail to Reject' Means in a Hypothesis Test
• How To Design a Science Fair Experiment
• What Is an Experiment? Definition and Design
• Hypothesis Test for the Difference of Two Population Proportions

Research Hypothesis In Psychology: Types, & Examples

Saul Mcleod, PhD

Editor-in-Chief for Simply Psychology

BSc (Hons) Psychology, MRes, PhD, University of Manchester

Saul Mcleod, PhD., is a qualified psychology teacher with over 18 years of experience in further and higher education. He has been published in peer-reviewed journals, including the Journal of Clinical Psychology.

Learn about our Editorial Process

Olivia Guy-Evans, MSc

Associate Editor for Simply Psychology

BSc (Hons) Psychology, MSc Psychology of Education

Olivia Guy-Evans is a writer and associate editor for Simply Psychology. She has previously worked in healthcare and educational sectors.

On This Page:

A research hypothesis, in its plural form “hypotheses,” is a specific, testable prediction about the anticipated results of a study, established at its outset. It is a key component of the scientific method .

Hypotheses connect theory to data and guide the research process towards expanding scientific understanding

Some key points about hypotheses:

• A hypothesis expresses an expected pattern or relationship. It connects the variables under investigation.
• It is stated in clear, precise terms before any data collection or analysis occurs. This makes the hypothesis testable.
• A hypothesis must be falsifiable. It should be possible, even if unlikely in practice, to collect data that disconfirms rather than supports the hypothesis.
• Hypotheses guide research. Scientists design studies to explicitly evaluate hypotheses about how nature works.
• For a hypothesis to be valid, it must be testable against empirical evidence. The evidence can then confirm or disprove the testable predictions.
• Hypotheses are informed by background knowledge and observation, but go beyond what is already known to propose an explanation of how or why something occurs.

Predictions typically arise from a thorough knowledge of the research literature, curiosity about real-world problems or implications, and integrating this to advance theory. They build on existing literature while providing new insight.

Types of Research Hypotheses

Alternative hypothesis.

The research hypothesis is often called the alternative or experimental hypothesis in experimental research.

It typically suggests a potential relationship between two key variables: the independent variable, which the researcher manipulates, and the dependent variable, which is measured based on those changes.

The alternative hypothesis states a relationship exists between the two variables being studied (one variable affects the other).

A hypothesis is a testable statement or prediction about the relationship between two or more variables. It is a key component of the scientific method. Some key points about hypotheses:

• Important hypotheses lead to predictions that can be tested empirically. The evidence can then confirm or disprove the testable predictions.

In summary, a hypothesis is a precise, testable statement of what researchers expect to happen in a study and why. Hypotheses connect theory to data and guide the research process towards expanding scientific understanding.

An experimental hypothesis predicts what change(s) will occur in the dependent variable when the independent variable is manipulated.

It states that the results are not due to chance and are significant in supporting the theory being investigated.

The alternative hypothesis can be directional, indicating a specific direction of the effect, or non-directional, suggesting a difference without specifying its nature. It’s what researchers aim to support or demonstrate through their study.

Null Hypothesis

The null hypothesis states no relationship exists between the two variables being studied (one variable does not affect the other). There will be no changes in the dependent variable due to manipulating the independent variable.

It states results are due to chance and are not significant in supporting the idea being investigated.

The null hypothesis, positing no effect or relationship, is a foundational contrast to the research hypothesis in scientific inquiry. It establishes a baseline for statistical testing, promoting objectivity by initiating research from a neutral stance.

Many statistical methods are tailored to test the null hypothesis, determining the likelihood of observed results if no true effect exists.

This dual-hypothesis approach provides clarity, ensuring that research intentions are explicit, and fosters consistency across scientific studies, enhancing the standardization and interpretability of research outcomes.

Nondirectional Hypothesis

A non-directional hypothesis, also known as a two-tailed hypothesis, predicts that there is a difference or relationship between two variables but does not specify the direction of this relationship.

It merely indicates that a change or effect will occur without predicting which group will have higher or lower values.

For example, “There is a difference in performance between Group A and Group B” is a non-directional hypothesis.

Directional Hypothesis

A directional (one-tailed) hypothesis predicts the nature of the effect of the independent variable on the dependent variable. It predicts in which direction the change will take place. (i.e., greater, smaller, less, more)

It specifies whether one variable is greater, lesser, or different from another, rather than just indicating that there’s a difference without specifying its nature.

For example, “Exercise increases weight loss” is a directional hypothesis.

Falsifiability

The Falsification Principle, proposed by Karl Popper , is a way of demarcating science from non-science. It suggests that for a theory or hypothesis to be considered scientific, it must be testable and irrefutable.

Falsifiability emphasizes that scientific claims shouldn’t just be confirmable but should also have the potential to be proven wrong.

It means that there should exist some potential evidence or experiment that could prove the proposition false.

However many confirming instances exist for a theory, it only takes one counter observation to falsify it. For example, the hypothesis that “all swans are white,” can be falsified by observing a black swan.

For Popper, science should attempt to disprove a theory rather than attempt to continually provide evidence to support a research hypothesis.

Can a Hypothesis be Proven?

Hypotheses make probabilistic predictions. They state the expected outcome if a particular relationship exists. However, a study result supporting a hypothesis does not definitively prove it is true.

All studies have limitations. There may be unknown confounding factors or issues that limit the certainty of conclusions. Additional studies may yield different results.

In science, hypotheses can realistically only be supported with some degree of confidence, not proven. The process of science is to incrementally accumulate evidence for and against hypothesized relationships in an ongoing pursuit of better models and explanations that best fit the empirical data. But hypotheses remain open to revision and rejection if that is where the evidence leads.

• Disproving a hypothesis is definitive. Solid disconfirmatory evidence will falsify a hypothesis and require altering or discarding it based on the evidence.
• However, confirming evidence is always open to revision. Other explanations may account for the same results, and additional or contradictory evidence may emerge over time.

We can never 100% prove the alternative hypothesis. Instead, we see if we can disprove, or reject the null hypothesis.

If we reject the null hypothesis, this doesn’t mean that our alternative hypothesis is correct but does support the alternative/experimental hypothesis.

Upon analysis of the results, an alternative hypothesis can be rejected or supported, but it can never be proven to be correct. We must avoid any reference to results proving a theory as this implies 100% certainty, and there is always a chance that evidence may exist which could refute a theory.

How to Write a Hypothesis

• Identify variables . The researcher manipulates the independent variable and the dependent variable is the measured outcome.
• Operationalized the variables being investigated . Operationalization of a hypothesis refers to the process of making the variables physically measurable or testable, e.g. if you are about to study aggression, you might count the number of punches given by participants.
• Decide on a direction for your prediction . If there is evidence in the literature to support a specific effect of the independent variable on the dependent variable, write a directional (one-tailed) hypothesis. If there are limited or ambiguous findings in the literature regarding the effect of the independent variable on the dependent variable, write a non-directional (two-tailed) hypothesis.
• Make it Testable : Ensure your hypothesis can be tested through experimentation or observation. It should be possible to prove it false (principle of falsifiability).
• Clear & concise language . A strong hypothesis is concise (typically one to two sentences long), and formulated using clear and straightforward language, ensuring it’s easily understood and testable.

Consider a hypothesis many teachers might subscribe to: students work better on Monday morning than on Friday afternoon (IV=Day, DV= Standard of work).

Now, if we decide to study this by giving the same group of students a lesson on a Monday morning and a Friday afternoon and then measuring their immediate recall of the material covered in each session, we would end up with the following:

• The alternative hypothesis states that students will recall significantly more information on a Monday morning than on a Friday afternoon.
• The null hypothesis states that there will be no significant difference in the amount recalled on a Monday morning compared to a Friday afternoon. Any difference will be due to chance or confounding factors.

More Examples

• Memory : Participants exposed to classical music during study sessions will recall more items from a list than those who studied in silence.
• Social Psychology : Individuals who frequently engage in social media use will report higher levels of perceived social isolation compared to those who use it infrequently.
• Developmental Psychology : Children who engage in regular imaginative play have better problem-solving skills than those who don’t.
• Clinical Psychology : Cognitive-behavioral therapy will be more effective in reducing symptoms of anxiety over a 6-month period compared to traditional talk therapy.
• Cognitive Psychology : Individuals who multitask between various electronic devices will have shorter attention spans on focused tasks than those who single-task.
• Health Psychology : Patients who practice mindfulness meditation will experience lower levels of chronic pain compared to those who don’t meditate.
• Organizational Psychology : Employees in open-plan offices will report higher levels of stress than those in private offices.
• Behavioral Psychology : Rats rewarded with food after pressing a lever will press it more frequently than rats who receive no reward.

What is a scientific hypothesis?

It's the initial building block in the scientific method.

Hypothesis basics

What makes a hypothesis testable.

• Types of hypotheses
• Hypothesis versus theory

Additional resources

Bibliography.

A scientific hypothesis is a tentative, testable explanation for a phenomenon in the natural world. It's the initial building block in the scientific method . Many describe it as an "educated guess" based on prior knowledge and observation. While this is true, a hypothesis is more informed than a guess. While an "educated guess" suggests a random prediction based on a person's expertise, developing a hypothesis requires active observation and background research. 

The basic idea of a hypothesis is that there is no predetermined outcome. For a solution to be termed a scientific hypothesis, it has to be an idea that can be supported or refuted through carefully crafted experimentation or observation. This concept, called falsifiability and testability, was advanced in the mid-20th century by Austrian-British philosopher Karl Popper in his famous book "The Logic of Scientific Discovery" (Routledge, 1959).

A key function of a hypothesis is to derive predictions about the results of future experiments and then perform those experiments to see whether they support the predictions.

A hypothesis is usually written in the form of an if-then statement, which gives a possibility (if) and explains what may happen because of the possibility (then). The statement could also include "may," according to California State University, Bakersfield .

Here are some examples of hypothesis statements:

• If garlic repels fleas, then a dog that is given garlic every day will not get fleas.
• If sugar causes cavities, then people who eat a lot of candy may be more prone to cavities.
• If ultraviolet light can damage the eyes, then maybe this light can cause blindness.

A useful hypothesis should be testable and falsifiable. That means that it should be possible to prove it wrong. A theory that can't be proved wrong is nonscientific, according to Karl Popper's 1963 book " Conjectures and Refutations ."

An example of an untestable statement is, "Dogs are better than cats." That's because the definition of "better" is vague and subjective. However, an untestable statement can be reworded to make it testable. For example, the previous statement could be changed to this: "Owning a dog is associated with higher levels of physical fitness than owning a cat." With this statement, the researcher can take measures of physical fitness from dog and cat owners and compare the two.

Types of scientific hypotheses

In an experiment, researchers generally state their hypotheses in two ways. The null hypothesis predicts that there will be no relationship between the variables tested, or no difference between the experimental groups. The alternative hypothesis predicts the opposite: that there will be a difference between the experimental groups. This is usually the hypothesis scientists are most interested in, according to the University of Miami .

For example, a null hypothesis might state, "There will be no difference in the rate of muscle growth between people who take a protein supplement and people who don't." The alternative hypothesis would state, "There will be a difference in the rate of muscle growth between people who take a protein supplement and people who don't."

If the results of the experiment show a relationship between the variables, then the null hypothesis has been rejected in favor of the alternative hypothesis, according to the book " Research Methods in Psychology " (​​BCcampus, 2015). 

There are other ways to describe an alternative hypothesis. The alternative hypothesis above does not specify a direction of the effect, only that there will be a difference between the two groups. That type of prediction is called a two-tailed hypothesis. If a hypothesis specifies a certain direction — for example, that people who take a protein supplement will gain more muscle than people who don't — it is called a one-tailed hypothesis, according to William M. K. Trochim , a professor of Policy Analysis and Management at Cornell University.

Sometimes, errors take place during an experiment. These errors can happen in one of two ways. A type I error is when the null hypothesis is rejected when it is true. This is also known as a false positive. A type II error occurs when the null hypothesis is not rejected when it is false. This is also known as a false negative, according to the University of California, Berkeley . 

A hypothesis can be rejected or modified, but it can never be proved correct 100% of the time. For example, a scientist can form a hypothesis stating that if a certain type of tomato has a gene for red pigment, that type of tomato will be red. During research, the scientist then finds that each tomato of this type is red. Though the findings confirm the hypothesis, there may be a tomato of that type somewhere in the world that isn't red. Thus, the hypothesis is true, but it may not be true 100% of the time.

Scientific theory vs. scientific hypothesis

The best hypotheses are simple. They deal with a relatively narrow set of phenomena. But theories are broader; they generally combine multiple hypotheses into a general explanation for a wide range of phenomena, according to the University of California, Berkeley . For example, a hypothesis might state, "If animals adapt to suit their environments, then birds that live on islands with lots of seeds to eat will have differently shaped beaks than birds that live on islands with lots of insects to eat." After testing many hypotheses like these, Charles Darwin formulated an overarching theory: the theory of evolution by natural selection.

"Theories are the ways that we make sense of what we observe in the natural world," Tanner said. "Theories are structures of ideas that explain and interpret facts." 

• Read more about writing a hypothesis, from the American Medical Writers Association.
• Find out why a hypothesis isn't always necessary in science, from The American Biology Teacher.
• Learn about null and alternative hypotheses, from Prof. Essa on YouTube .

Encyclopedia Britannica. Scientific Hypothesis. Jan. 13, 2022. https://www.britannica.com/science/scientific-hypothesis

Karl Popper, "The Logic of Scientific Discovery," Routledge, 1959.

California State University, Bakersfield, "Formatting a testable hypothesis." https://www.csub.edu/~ddodenhoff/Bio100/Bio100sp04/formattingahypothesis.htm  

Karl Popper, "Conjectures and Refutations," Routledge, 1963.

Price, P., Jhangiani, R., & Chiang, I., "Research Methods of Psychology — 2nd Canadian Edition," BCcampus, 2015.‌

University of Miami, "The Scientific Method" http://www.bio.miami.edu/dana/161/evolution/161app1_scimethod.pdf  

William M.K. Trochim, "Research Methods Knowledge Base," https://conjointly.com/kb/hypotheses-explained/  

University of California, Berkeley, "Multiple Hypothesis Testing and False Discovery Rate" https://www.stat.berkeley.edu/~hhuang/STAT141/Lecture-FDR.pdf  

University of California, Berkeley, "Science at multiple levels" https://undsci.berkeley.edu/article/0_0_0/howscienceworks_19

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Hypothesis vs. Prediction

What's the difference.

Hypothesis and prediction are both important components of the scientific method, but they serve different purposes. A hypothesis is a proposed explanation or statement that can be tested through experimentation or observation. It is based on prior knowledge, observations, or theories and is used to guide scientific research. On the other hand, a prediction is a specific statement about what will happen in a particular situation or experiment. It is often derived from a hypothesis and serves as a testable outcome that can be confirmed or refuted through data analysis. While a hypothesis provides a broader framework for scientific inquiry, a prediction is a more specific and measurable expectation of the results.

Further Detail

Introduction.

When it comes to scientific research and inquiry, two important concepts that often come into play are hypothesis and prediction. Both of these terms are used to make educated guesses or assumptions about the outcome of an experiment or study. While they share some similarities, they also have distinct attributes that set them apart. In this article, we will explore the characteristics of hypothesis and prediction, highlighting their differences and similarities.

A hypothesis is a proposed explanation or statement that can be tested through experimentation or observation. It is typically formulated based on existing knowledge, observations, or theories. A hypothesis is often used as a starting point for scientific research, as it provides a framework for investigation and helps guide the research process.

One of the key attributes of a hypothesis is that it is testable. This means that it can be subjected to empirical evidence and observations to determine its validity. A hypothesis should be specific and measurable, allowing researchers to design experiments or gather data to either support or refute the hypothesis.

Another important aspect of a hypothesis is that it is falsifiable. This means that it is possible to prove the hypothesis wrong through experimentation or observation. Falsifiability is crucial in scientific research, as it ensures that hypotheses can be objectively tested and evaluated.

Hypotheses can be classified into two main types: null hypotheses and alternative hypotheses. A null hypothesis states that there is no significant relationship or difference between variables, while an alternative hypothesis proposes the existence of a relationship or difference. These two types of hypotheses are often used in statistical analysis to draw conclusions from data.

In summary, a hypothesis is a testable and falsifiable statement that serves as a starting point for scientific research. It is specific, measurable, and can be either a null or alternative hypothesis.

While a hypothesis is a proposed explanation or statement, a prediction is a specific outcome or result that is anticipated based on existing knowledge or theories. Predictions are often made before conducting an experiment or study and serve as a way to anticipate the expected outcome.

Unlike a hypothesis, a prediction is not necessarily testable or falsifiable on its own. Instead, it is used to guide the research process and provide a basis for comparison with the actual results obtained from the experiment or study. Predictions can be based on previous research, theoretical models, or logical reasoning.

One of the key attributes of a prediction is that it is specific and precise. It should clearly state the expected outcome or result, leaving little room for ambiguity. This allows researchers to compare the prediction with the actual results and evaluate the accuracy of their anticipated outcome.

Predictions can also be used to generate hypotheses. By making a prediction and comparing it with the actual results, researchers can identify discrepancies or unexpected findings. These observations can then be used to formulate new hypotheses and guide further research.

In summary, a prediction is a specific anticipated outcome or result that is not necessarily testable or falsifiable on its own. It serves as a basis for comparison with the actual results obtained from an experiment or study and can be used to generate new hypotheses.

Similarities

While hypotheses and predictions have distinct attributes, they also share some similarities in the context of scientific research. Both hypotheses and predictions are based on existing knowledge, observations, or theories. They are both used to make educated guesses or assumptions about the outcome of an experiment or study.

Furthermore, both hypotheses and predictions play a crucial role in the scientific method. They provide a framework for research, guiding the design of experiments, data collection, and analysis. Both hypotheses and predictions are subject to evaluation and revision based on empirical evidence and observations.

Additionally, both hypotheses and predictions can be used to generate new knowledge and advance scientific understanding. By testing hypotheses and comparing predictions with actual results, researchers can gain insights into the relationships between variables, uncover new phenomena, or challenge existing theories.

Overall, while hypotheses and predictions have their own unique attributes, they are both integral components of scientific research and inquiry.

In conclusion, hypotheses and predictions are important concepts in scientific research. While a hypothesis is a testable and falsifiable statement that serves as a starting point for investigation, a prediction is a specific anticipated outcome or result that guides the research process. Hypotheses are specific, measurable, and can be either null or alternative, while predictions are precise and serve as a basis for comparison with actual results.

Despite their differences, hypotheses and predictions share similarities in terms of their reliance on existing knowledge, their role in the scientific method, and their potential to generate new knowledge. Both hypotheses and predictions contribute to the advancement of scientific understanding and play a crucial role in the research process.

By understanding the attributes of hypotheses and predictions, researchers can effectively formulate research questions, design experiments, and analyze data. These concepts are fundamental to the scientific method and are essential for the progress of scientific research and inquiry.

Comparisons may contain inaccurate information about people, places, or facts. Please report any issues.

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Science Struck

What’s the Real Difference Between Hypothesis and Prediction

Both hypothesis and prediction fall in the realm of guesswork, but with different assumptions. This Buzzle write-up below will elaborate on the differences between hypothesis and prediction.

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“There is no justifiable prediction about how the hypothesis will hold up in the future; its degree of corroboration simply is a historical statement describing how severely the hypothesis has been tested in the past.” ― Robert Nozick, American author, professor, and philosopher

A lot of people tend to think that a hypothesis is the same as prediction, but this is not true. They are entirely different terms, though they can be manifested within the same example. They are both entities that stem from statistics, and are used in a variety of applications like finance, mathematics, science (widely), sports, psychology, etc. A hypothesis may be a prediction, but the reverse may not be true.

Also, a prediction may or may not agree with the hypothesis. Confused? Don’t worry, read the hypothesis vs. prediction comparison, provided below with examples, to clear your doubts regarding both these entities.

• A hypothesis is a kind of guess or proposition regarding a situation.
• It can be called a kind of intelligent guess or prediction, and it needs to be proved using different methods.
• Formulating a hypothesis is an important step in experimental design, for it helps to predict things that might take place in the course of research.
• The strength of the statement is based on how effectively it is proved while conducting experiments.
• It is usually written in the ‘If-then-because’ format.
• For example, ‘ If Susan’s mood depends on the weather, then she will be happy today, because it is bright and sunny outside. ‘. Here, Susan’s mood is the dependent variable, and the weather is the independent variable. Thus, a hypothesis helps establish a relationship.
• A prediction is also a type of guess, in fact, it is a guesswork in the true sense of the word.
• It is not an educated guess, like a hypothesis, i.e., it is based on established facts.
• While making a prediction for various applications, you have to take into account all the current observations.
• It can be testable, but just once. This goes to prove that the strength of the statement is based on whether the predicted event occurs or not.
• It is harder to define, and it contains many variations, which is why, probably, it is confused to be a fictional guess or forecast.
• For example, He is studying very hard, he might score an A . Here, we are predicting that since the student is working hard, he might score good marks. It is based on an observation and does not establish any relationship.

Factors of Differentiation

♦ Consider a statement, ‘If I add some chili powder, the pasta may become spicy’. This is a hypothesis, and a testable statement. You can carry on adding 1 pinch of chili powder, or a spoon, or two spoons, and so on. The dish may become spicier or pungent, or there may be no reaction at all. The sum and substance is that, the amount of chili powder is the independent variable here, and the pasta dish is the dependent variable, which is expected to change with the addition of chili powder. This statement thus establishes and analyzes the relationship between both variables, and you will get a variety of results when the test is performed multiple times. Your hypothesis may even be opposed tomorrow.

♦ Consider the statement, ‘Robert has longer legs, he may run faster’. This is just a prediction. You may have read somewhere that people with long legs tend to run faster. It may or may not be true. What is important here is ‘Robert’. You are talking only of Robert’s legs, so you will test if he runs faster. If he does, your prediction is true, if he doesn’t, your prediction is false. No more testing.

♦ Consider a statement, ‘If you eat chocolates, you may get acne’. This is a simple hypothesis, based on facts, yet necessary to be proven. It can be tested on a number of people. It may be true, it may be false. The fact is, it defines a relationship between chocolates and acne. The relationship can be analyzed and the results can be recorded. Tomorrow, someone might come up with an alternative hypothesis that chocolate does not cause acne. This will need to be tested again, and so on. A hypothesis is thus, something that you think happens due to a reason.

♦ Consider a statement, ‘The sky is overcast, it may rain today’. A simple guess, based on the fact that it generally rains if the sky is overcast. It may not even be testable, i.e., the sky can be overcast now and clear the next minute. If it does rain, you have predicted correctly. If it does not, you are wrong. No further analysis or questions.

Both hypothesis and prediction need to be effectively structured so that further analysis of the problem statement is easier. Remember that, the key difference between the two is the procedure of proving the statements. Also, you cannot state one is better than the other, this depends entirely on the application in hand.

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Home » What is a Hypothesis – Types, Examples and Writing Guide

What is a Hypothesis – Types, Examples and Writing Guide

Table of Contents

Definition:

Hypothesis is an educated guess or proposed explanation for a phenomenon, based on some initial observations or data. It is a tentative statement that can be tested and potentially proven or disproven through further investigation and experimentation.

Hypothesis is often used in scientific research to guide the design of experiments and the collection and analysis of data. It is an essential element of the scientific method, as it allows researchers to make predictions about the outcome of their experiments and to test those predictions to determine their accuracy.

Types of Hypothesis

Types of Hypothesis are as follows:

Research Hypothesis

A research hypothesis is a statement that predicts a relationship between variables. It is usually formulated as a specific statement that can be tested through research, and it is often used in scientific research to guide the design of experiments.

Null Hypothesis

The null hypothesis is a statement that assumes there is no significant difference or relationship between variables. It is often used as a starting point for testing the research hypothesis, and if the results of the study reject the null hypothesis, it suggests that there is a significant difference or relationship between variables.

Alternative Hypothesis

An alternative hypothesis is a statement that assumes there is a significant difference or relationship between variables. It is often used as an alternative to the null hypothesis and is tested against the null hypothesis to determine which statement is more accurate.

Directional Hypothesis

A directional hypothesis is a statement that predicts the direction of the relationship between variables. For example, a researcher might predict that increasing the amount of exercise will result in a decrease in body weight.

Non-directional Hypothesis

A non-directional hypothesis is a statement that predicts the relationship between variables but does not specify the direction. For example, a researcher might predict that there is a relationship between the amount of exercise and body weight, but they do not specify whether increasing or decreasing exercise will affect body weight.

Statistical Hypothesis

A statistical hypothesis is a statement that assumes a particular statistical model or distribution for the data. It is often used in statistical analysis to test the significance of a particular result.

Composite Hypothesis

A composite hypothesis is a statement that assumes more than one condition or outcome. It can be divided into several sub-hypotheses, each of which represents a different possible outcome.

Empirical Hypothesis

An empirical hypothesis is a statement that is based on observed phenomena or data. It is often used in scientific research to develop theories or models that explain the observed phenomena.

Simple Hypothesis

A simple hypothesis is a statement that assumes only one outcome or condition. It is often used in scientific research to test a single variable or factor.

Complex Hypothesis

A complex hypothesis is a statement that assumes multiple outcomes or conditions. It is often used in scientific research to test the effects of multiple variables or factors on a particular outcome.

Applications of Hypothesis

Hypotheses are used in various fields to guide research and make predictions about the outcomes of experiments or observations. Here are some examples of how hypotheses are applied in different fields:

• Science : In scientific research, hypotheses are used to test the validity of theories and models that explain natural phenomena. For example, a hypothesis might be formulated to test the effects of a particular variable on a natural system, such as the effects of climate change on an ecosystem.
• Medicine : In medical research, hypotheses are used to test the effectiveness of treatments and therapies for specific conditions. For example, a hypothesis might be formulated to test the effects of a new drug on a particular disease.
• Psychology : In psychology, hypotheses are used to test theories and models of human behavior and cognition. For example, a hypothesis might be formulated to test the effects of a particular stimulus on the brain or behavior.
• Sociology : In sociology, hypotheses are used to test theories and models of social phenomena, such as the effects of social structures or institutions on human behavior. For example, a hypothesis might be formulated to test the effects of income inequality on crime rates.
• Business : In business research, hypotheses are used to test the validity of theories and models that explain business phenomena, such as consumer behavior or market trends. For example, a hypothesis might be formulated to test the effects of a new marketing campaign on consumer buying behavior.
• Engineering : In engineering, hypotheses are used to test the effectiveness of new technologies or designs. For example, a hypothesis might be formulated to test the efficiency of a new solar panel design.

How to write a Hypothesis

Here are the steps to follow when writing a hypothesis:

Identify the Research Question

The first step is to identify the research question that you want to answer through your study. This question should be clear, specific, and focused. It should be something that can be investigated empirically and that has some relevance or significance in the field.

Conduct a Literature Review

Before writing your hypothesis, it’s essential to conduct a thorough literature review to understand what is already known about the topic. This will help you to identify the research gap and formulate a hypothesis that builds on existing knowledge.

Determine the Variables

The next step is to identify the variables involved in the research question. A variable is any characteristic or factor that can vary or change. There are two types of variables: independent and dependent. The independent variable is the one that is manipulated or changed by the researcher, while the dependent variable is the one that is measured or observed as a result of the independent variable.

Formulate the Hypothesis

Based on the research question and the variables involved, you can now formulate your hypothesis. A hypothesis should be a clear and concise statement that predicts the relationship between the variables. It should be testable through empirical research and based on existing theory or evidence.

Write the Null Hypothesis

The null hypothesis is the opposite of the alternative hypothesis, which is the hypothesis that you are testing. The null hypothesis states that there is no significant difference or relationship between the variables. It is important to write the null hypothesis because it allows you to compare your results with what would be expected by chance.

Refine the Hypothesis

After formulating the hypothesis, it’s important to refine it and make it more precise. This may involve clarifying the variables, specifying the direction of the relationship, or making the hypothesis more testable.

Examples of Hypothesis

Here are a few examples of hypotheses in different fields:

• Psychology : “Increased exposure to violent video games leads to increased aggressive behavior in adolescents.”
• Biology : “Higher levels of carbon dioxide in the atmosphere will lead to increased plant growth.”
• Sociology : “Individuals who grow up in households with higher socioeconomic status will have higher levels of education and income as adults.”
• Education : “Implementing a new teaching method will result in higher student achievement scores.”
• Marketing : “Customers who receive a personalized email will be more likely to make a purchase than those who receive a generic email.”
• Physics : “An increase in temperature will cause an increase in the volume of a gas, assuming all other variables remain constant.”
• Medicine : “Consuming a diet high in saturated fats will increase the risk of developing heart disease.”

Purpose of Hypothesis

The purpose of a hypothesis is to provide a testable explanation for an observed phenomenon or a prediction of a future outcome based on existing knowledge or theories. A hypothesis is an essential part of the scientific method and helps to guide the research process by providing a clear focus for investigation. It enables scientists to design experiments or studies to gather evidence and data that can support or refute the proposed explanation or prediction.

The formulation of a hypothesis is based on existing knowledge, observations, and theories, and it should be specific, testable, and falsifiable. A specific hypothesis helps to define the research question, which is important in the research process as it guides the selection of an appropriate research design and methodology. Testability of the hypothesis means that it can be proven or disproven through empirical data collection and analysis. Falsifiability means that the hypothesis should be formulated in such a way that it can be proven wrong if it is incorrect.

In addition to guiding the research process, the testing of hypotheses can lead to new discoveries and advancements in scientific knowledge. When a hypothesis is supported by the data, it can be used to develop new theories or models to explain the observed phenomenon. When a hypothesis is not supported by the data, it can help to refine existing theories or prompt the development of new hypotheses to explain the phenomenon.

When to use Hypothesis

Here are some common situations in which hypotheses are used:

• In scientific research , hypotheses are used to guide the design of experiments and to help researchers make predictions about the outcomes of those experiments.
• In social science research , hypotheses are used to test theories about human behavior, social relationships, and other phenomena.
• I n business , hypotheses can be used to guide decisions about marketing, product development, and other areas. For example, a hypothesis might be that a new product will sell well in a particular market, and this hypothesis can be tested through market research.

Characteristics of Hypothesis

Here are some common characteristics of a hypothesis:

• Testable : A hypothesis must be able to be tested through observation or experimentation. This means that it must be possible to collect data that will either support or refute the hypothesis.
• Falsifiable : A hypothesis must be able to be proven false if it is not supported by the data. If a hypothesis cannot be falsified, then it is not a scientific hypothesis.
• Clear and concise : A hypothesis should be stated in a clear and concise manner so that it can be easily understood and tested.
• Based on existing knowledge : A hypothesis should be based on existing knowledge and research in the field. It should not be based on personal beliefs or opinions.
• Specific : A hypothesis should be specific in terms of the variables being tested and the predicted outcome. This will help to ensure that the research is focused and well-designed.
• Tentative: A hypothesis is a tentative statement or assumption that requires further testing and evidence to be confirmed or refuted. It is not a final conclusion or assertion.
• Relevant : A hypothesis should be relevant to the research question or problem being studied. It should address a gap in knowledge or provide a new perspective on the issue.

Advantages of Hypothesis

Hypotheses have several advantages in scientific research and experimentation:

• Guides research: A hypothesis provides a clear and specific direction for research. It helps to focus the research question, select appropriate methods and variables, and interpret the results.
• Predictive powe r: A hypothesis makes predictions about the outcome of research, which can be tested through experimentation. This allows researchers to evaluate the validity of the hypothesis and make new discoveries.
• Facilitates communication: A hypothesis provides a common language and framework for scientists to communicate with one another about their research. This helps to facilitate the exchange of ideas and promotes collaboration.
• Efficient use of resources: A hypothesis helps researchers to use their time, resources, and funding efficiently by directing them towards specific research questions and methods that are most likely to yield results.
• Provides a basis for further research: A hypothesis that is supported by data provides a basis for further research and exploration. It can lead to new hypotheses, theories, and discoveries.
• Increases objectivity: A hypothesis can help to increase objectivity in research by providing a clear and specific framework for testing and interpreting results. This can reduce bias and increase the reliability of research findings.

Limitations of Hypothesis

Some Limitations of the Hypothesis are as follows:

• Limited to observable phenomena: Hypotheses are limited to observable phenomena and cannot account for unobservable or intangible factors. This means that some research questions may not be amenable to hypothesis testing.
• May be inaccurate or incomplete: Hypotheses are based on existing knowledge and research, which may be incomplete or inaccurate. This can lead to flawed hypotheses and erroneous conclusions.
• May be biased: Hypotheses may be biased by the researcher’s own beliefs, values, or assumptions. This can lead to selective interpretation of data and a lack of objectivity in research.
• Cannot prove causation: A hypothesis can only show a correlation between variables, but it cannot prove causation. This requires further experimentation and analysis.
• Limited to specific contexts: Hypotheses are limited to specific contexts and may not be generalizable to other situations or populations. This means that results may not be applicable in other contexts or may require further testing.
• May be affected by chance : Hypotheses may be affected by chance or random variation, which can obscure or distort the true relationship between variables.

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• Key Differences

Know the Differences & Comparisons

Difference Between Hypothesis and Prediction

Due to insufficient knowledge, many misconstrue hypothesis for prediction, which is wrong, as these two are entirely different. Prediction is forecasting of future events, which is sometimes based on evidence or sometimes, on a person’s instinct or gut feeling. So take a glance at the article presented below, which elaborates the difference between hypothesis and prediction.

Content: Hypothesis Vs Prediction

Comparison chart, definition of hypothesis.

In simple terms, hypothesis means a sheer assumption which can be approved or disapproved. For the purpose of research, the hypothesis is defined as a predictive statement, which can be tested and verified using the scientific method. By testing the hypothesis, the researcher can make probability statements on the population parameter. The objective of the hypothesis is to find the solution to a given problem.

A hypothesis is a mere proposition which is put to the test to ascertain its validity. It states the relationship between an independent variable to some dependent variable. The characteristics of the hypothesis are described as under:

• It should be clear and precise.
• It should be stated simply.
• It must be specific.
• It should correlate variables.
• It should be consistent with most known facts.
• It should be capable of being tested.
• It must explain, what it claims to explain.

Definition of Prediction

A prediction is described as a statement which forecasts a future event, which may or may not be based on knowledge and experience, i.e. it can be a pure guess based on the instinct of a person. It is termed as an informed guess, when the prediction comes out from a person having ample subject knowledge and uses accurate data and logical reasoning, to make it.

Regression analysis is one of the statistical technique, which is used for making the prediction.

In many multinational corporations, futurists (predictors) are paid a good amount for making prediction relating to the possible events, opportunities, threats or risks. And to do so, the futurists, study all past and current events, to forecast future occurrences. Further, it has a great role to play in statistics also, to draw inferences about a population parameter.

Key Differences Between Hypothesis and Prediction

The difference between hypothesis and prediction can be drawn clearly on the following grounds:

• A propounded explanation for an observable occurrence, established on the basis of established facts, as an introduction to the further study, is known as the hypothesis. A statement, which tells or estimates something that will occur in future is known as the prediction.
• The hypothesis is nothing but a tentative supposition which can be tested by scientific methods. On the contrary, the prediction is a sort of declaration made in advance on what is expected to happen next, in the sequence of events.
• While the hypothesis is an intelligent guess, the prediction is a wild guess.
• A hypothesis is always supported by facts and evidence. As against this, predictions are based on knowledge and experience of the person making it, but that too not always.
• Hypothesis always have an explanation or reason, whereas prediction does not have any explanation.
• Hypothesis formulation takes a long time. Conversely, making predictions about a future happening does not take much time.
• Hypothesis defines a phenomenon, which may be a future or a past event. Unlike, prediction, which always anticipates about happening or non-happening of a certain event in future.
• The hypothesis states the relationship between independent variable and the dependent variable. On the other hand, prediction does not state any relationship between variables.

To sum up, the prediction is merely a conjecture to discern future, while a hypothesis is a proposition put forward for the explanation. The former, can be made by any person, no matter he/she has knowledge in the particular field. On the flip side, the hypothesis is made by the researcher to discover the answer to a certain question. Further, the hypothesis has to pass to various test, to become a theory.

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15 Times Scientists Were Right And We Didn’t Listen

Posted: April 23, 2024 | Last updated: April 23, 2024

Have you ever watched a historical drama in which a scientist tries to convince a group of people who are set in their ways that germs are real or that disease isn’t spread via bad smells? You’re most likely sitting there, shaking your head, thinking how foolish these naysayers will look in the future.

The very nature of science is to observe, hypothesis, and experiment. New ideas can be hard to prove and are often met with resistance. So, it’s no surprise that there exists an extensive catalog of predictions by scientists that were initially disregarded. Here are just a few.

1. Climate Change Predictions

For decades, scientists have warned about the consequences of burning fossil fuels and climate change. Despite overwhelming evidence, many politicians and individuals have downplayed or denied the severity of the issue.

Since as early as 1896,  scientists  have been aware of climate change issues. Swedish scientist Svante Arrhenius predicted that fluctuations in atmospheric carbon dioxide levels could significantly impact surface temperatures through the greenhouse effect. Despite numerous scientists’ subsequent releases of substantial evidence, a segment of climate change deniers persists.

2. Cigarette Smoking and Cancer

Back in 1966, approximately 42% of the US adult population were cigarette smokers. Today, that number sits at 11.5%. By the 1950s,  scientists reported  that cigarettes were a cause of human disease, but this information was met with resistance by the tobacco companies and the general population.

In 2006, cigarette manufacturers were finally sued for their persistence in selling despite knowing the truth. Seventy years of public awareness and accountability are finally decreasing the number of smokers.

3. Germ Theory

Until the 19th century, it was widely believed that foul air, known as miasmas, was responsible for spreading diseases such as cholera and the bubonic plague. These miasmas were thought to be created from rotting matter.

While germ theory was known, it wasn’t until the late 19th century that it became an accepted truth. It wasn’t until Louis Pasteur’s work in 1861 that germ theory was accepted.

4. Dangers of Asbestos

Asbestos was found to harm human health starting in the early 20th century. However, widespread recognition of its dangers and regulatory actions took several decades. In 1934, researchers discovered a link between asbestos handling and disease.

The warnings about it didn’t come into effect until 1942. Despite this, the  asbestos industry  continued to thrive, with many denying or hiding the scientific evidence. Although most countries have  banned its use, the U.S. and Mexico continue to import it for some industries.

5. Overuse of Antibiotics

Scientists warned about the dangers of overprescribing antibiotics and the rise of antibiotic-resistant bacteria. Their warnings went unheeded by many, and widespread misuse of these drugs has led to the emergence of superbugs that are increasingly difficult to treat.

Many general practitioners prescribe antibiotics to people with colds and viruses, but these medicines have only a  placebo effect  on these conditions. Antibiotics are only effective in particular bacterial infections.

6. Extinction Crisis

Conservationists are often ignored and don’t receive sufficient attention or funding. Due to human activities such as deforestation, pollution, and habitat destruction, many species are being added to the endangered and extinction lists.

While some extinction is a natural phenomenon, scientists have shown that humans are accelerating its growth. A  report  from the WWF showed that animal species had reduced by 60% since the 1970s.

7. Theory of Evolution

Charles Darwin’s theory of evolution, published in 1859, contradicted what many religious and scientific circles believed at the time. Despite abundant evidence from fields such as paleontology, genetics, and comparative anatomy, acceptance of evolution took many years and continues to be contested by some.

Darwin’s theory revolutionized disciplines beyond biology.  Studies  have shown it helps us understand the fundamental principles of human life in other scientific fields, such as anthropology, psychology, and medicine. It also provides insight into the origin of diseases.

8. Diet and Health

Nutrition scientists have warned us against excessive consumption of processed foods, sugar, and unhealthy fats, linking them to obesity, diabetes, and heart disease. However, marketing campaigns promoting unhealthy products have often overshadowed these warnings.

With abundant conflicting information fed to us daily, knowing what is right and who to listen to can be difficult. It’s probably advisable to listen to science over the cereal jingle on the TV.

9. Lead Poisoning

Humankind has used lead for thousands of years. It’s been used to make coins, bullets, and armor. In the Middle Ages, lead was used in alchemy. In the Victorian era, it was widely used in cosmetics. In the 20th century, it was used in paint and gasoline.

Through science, we now know that lead poisoning can cause issues such as decreased mental capabilities, irritability, and, in acute cases, paralysis. The  Romans  knew there were lead issues, but that didn’t stop anyone from using it. It wasn’t until the 1970s that the EPA finally stopped its use in fuel and paint, and the public became more aware of the issue.

10. DNA and Heredity

The idea that genes encode hereditary traits faced initial skepticism. Yet, in the 1950s, scientists like James Watson and Francis Crick discovered the structure of DNA and its role in heredity, revolutionizing biology and genetics.

This discovery has helped immensely in the field of medicine. It can be used as a  preventative  measure and a diagnostic tool to determine someone’s susceptibility to particular conditions and diseases.

11. Plastics Pollution

The invention of plastics emerged in the early 20th century and gained popularity after World War II. By the 1960s and 70s, scientists started to become concerned about plastic pollution in the environment. Environmental scientists have warned about the pervasive problem of plastic pollution in oceans and ecosystems, which poses threats to marine life and human health.

Unfortunately, those in charge have taken a long time to raise awareness and make positive changes to reduce plastics. Some U.S. states take on the challenge of reducing plastics proactively, while others don’t have recycling programs.

12. Pesticide Use and Environmental Damage

Agricultural scientists have cautioned against the indiscriminate use of pesticides, which can harm beneficial insects, soil health, and water quality. However, intensive farming practices often prioritize short-term gains over long-term sustainability.

Take, for instance, the case of DDT, a potent pesticide once used extensively on crops. Despite its effectiveness in controlling pests, DDT’s persistence in the environment and its harmful impact on wildlife led to its eventual ban. Yet, the legacy of DDT continues to pose challenges as residues persist in ecosystems and still affect organisms today.

13. Overfishing and Marine Depletion

There’s a reason caviar is expensive—it’s hard to get because most sturgeon fish are on the endangered list. Marine biologists have warned about the depletion of fish stocks due to overfishing and destructive fishing methods.

The depletion of fish populations in the ocean disrupts the ecosystem’s delicate balance, leading to far-reaching consequences. Since many other marine species depend on fish as a food source, this depletion triggers a chain reaction throughout the ocean’s food web. Maybe it’s time to lay off the caviar.

14. Space Debris

Space is vast, so what’s a little space debris? Astronomers have raised concerns about the growing amount of space debris orbiting the Earth and estimate that about 34,000 pieces are floating around, which could cause a lot of damage if they were to hit something. Space junk is the result of humans shooting things into space.

Logistical and regulatory challenges have hampered efforts to retrieve space debris. Also, some methods — such as firing a missile to destroy a dead satellite — have been scrutinized, creating even more space junk.

15. Bloodletting

For thousands of years, many illnesses were treated by bloodletting. Several methods were used, one of which even included leeches. This may seem strange, given that having a good blood supply is essential for health. However, medical professionals believed it purged the body of sickness.

Studies  regarding bloodletting’s effectiveness were conducted in the 19th century. Of course, many were still skeptical about the results, and the method didn’t entirely lose favor until the end of the century.

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Zurich Classic Betting Preview: Odds, Prediction, Long Shot Bet, Team to Fade and Outright Winner Pick

• Author: Brian Giuffra

In this story:

The only PGA Tour team event of the season (think laidback Ryder Cup vibes) is here and it’s chock-full of powerhouse tandems to consider betting on. 

Rory McIlroy and Shane Lowry, Xander Schauffle and Patrick Cantlay, Will Zalatoris and Sahith Theegala headline the field this week, but we're not betting on them, choosing instead to focus on a team of brothers.

Before betting, it’s important to remember this is a unique format. There are two days of fourball, known as best-ball, played in Rounds 1 and 3 and two rounds of foursomes, known as alternate shots, in Rounds 2 and 4.

Last year’s winning team of Davis Riley and Nick Hardy shot 30 under for the week, so we must pick duos who can go low in each format.

Who are they? Let’s break it down starting with the Top 10 teams in odds, which come from DraftKings Sportsbook, followed by our longshot pick, the team to fade and outright winner. 

Zurich Classic Odds

Patrick Cantlay / Xander Schauffele +400

Rory McIlroy / Shane Lowry +700

Will Zalatoris / Sahith Theegala +800

Collin Morikawa / Kurt Kitayama +1200

Rasmus Højgaard / Nicolai Højgaard +2200

Tom Hoge / Maverick McNealy +2500

Matt Fitzpatrick / Alex Fitzpatrick +2500

Taylor Pendrith / Corey Conners +2800

Nick Taylor / Adam Hadwin +3000

Sepp Straka / Brice Garnett +4000

Keith Mitchell / Joel Dahmen +4000

Doug Ghim / Chan Kim +4000

​​Billy Horschel / Tyson Alexander +4000

Zurich Classic Long Shot Pick: ​​Billy Horschel / Tyson Alexander +4000

There’s no shortage of star power atop the leaderboard, but a team capable of competing this week that’s flying under the radar is Billy Horschel and Tyson Alexander at +4000 odds. 

Horschel just won the Puntacana Championship with a 23-under total highlighted by a 63 on Sunday. He’s always been a streaky player and I’m counting on his hot streak continuing at an event he’s had success at before. 

Horschel won this event when it was played as an individual tournament and won it another time as a team. He’s placed second twice and tied for fourth once here. Last year, he placed 11th with Sam Burns. 

Alexander is not as well known and doesn’t have as strong of a history at the Zurich, but he’s off to a good start this season (has made three straight cuts) and had his best start of the season three weeks ago at the Texas Open. He's also 49th on tour in shots-gained: putting, an important stat this week, especially in alternate shot.

Winning back-to-back events is tough, but Horschel’s hot and he wouldn’t have picked Alexander as his teammate in an event he has a history of success at if he didn’t think they could win. 

Zurich Classic Team to Fade: Rasmus Højgaard / Nicolai Højgaard +2200

Nicolai Højgaard was leading the Masters outright Saturday afternoon before a disastrous back nine 40 ended his chances of winning. He followed that up with a 4-over 76 on Sunday and just missed the cut at the Puntacana Championship.

Overall, he’s missed three cuts in his last five events and was T-75 in another with a +5 total score. His run at Augusta seems more an apparition when taking a macro view of his recent form.

Nicolai is playing with his twin brother, ​​Rasmus, who withdrew from the Indian Open after an opening-round 79. ​​Rasmus does have three Top 10s on the DP World Tour this season, but the most recent one came in February. 

Recent results suggest both brother’s forms are regressing heading into this one. While being together could lighten the mood and improve their play, the +2200 odds are not good enough to warrant a bet. 

Zurich Classic Pick to Win: Matt Fitzpatrick / Alex Fitzpatrick +2500

After fading one brother duo, why not back another, huh? 

Matt Fitzpatrick is ranked ninth in total driving on the PGA Tour, 26th in total putting, 15th in birdie average per round and 11th in birdie percentage on Par 5s. He finished fifth at the Players and 10th at the Texas Open, was in contention at the Masters early and just shot four rounds under par at the RBC Heritage. 

Alex finished T23 at the Corales last week with four rounds under par including a Sunday 67. He missed the cut at the Indian Open at the end of March, but has been steady on the DP World Tour this season, with six Top 20s in nine events. Like his brother, Alex is a solid driver and strong putter. 

Last year, the Fitzpatricks finished T-19 at the Zurich, with two rounds of 62 during fourball and rounds of 71 and 74 in alternate shot. If they can make up some strokes in the latter format, they’ll be in contention this week, because both make a ton of birdies off their own ball. 

Odds refresh periodically and are subject to change.

If you or someone you know has a gambling problem and wants help, call 1-800-GAMBLER.

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Pittsburgh Steelers NFL Draft 2024 guide: Picks, predictions and key needs

“The Beast ,” Dane Brugler’s expansive guide to the NFL Draft , is here. 

The Pittsburgh Steelers have the 20th pick in the NFL Draft when Round 1 begins April 25 in Detroit. The Steelers own seven total picks in the seven-round draft, including one pick in the first, one in the second and two in the third round for four picks inside the top 100.

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Steelers’ draft picks

Full draft order.

Every pick in the seven-round NFL Draft .

NFL Draft details

• Round 1: April 25, 8 p.m. ET • Rounds 2-3: April 26, 7 p.m. ET • Rounds 4-7: April 27, noon ET

All rounds will be televised on ESPN/ABC and NFL Network and in Spanish on ESPN Deportes.

About the Steelers

• Head coach: Mike Tomlin (18th season with team) • General manager: Omar Khan (second full season with team) • Last year’s record: 10-7 (lost at Buffalo in wild-card playoffs)

An opportunistic defense and a running game that came alive down the stretch carried the Steelers’ anemic offense into the postseason in 2023. This year, the formula should be similar. So long as T.J. Watt stays healthy, there’s reason to believe the defense will be as good (or better) than last year when it finished sixth-best in scoring. The addition of offensive coordinator Arthur Smith should highlight a talented running back tandem. The real question is: Can Russell Wilson or Justin Fields add a spark in the passing game to push the Steelers into the true contender category?

Steelers’ key position needs 

Center: When the Steelers cut Mason Cole , they created an obvious void in the middle of the offensive line. While reserve lineman Nate Herbig is center-capable, Pittsburgh will almost certainly address center in a more meaningful way, possibly as early as the first or second round. This is an especially good year to be looking for a center. As many as three — Oregon’s Jackson Powers-Johnson, Duke’s Graham Barton and West Virginia’s Zach Frazier — could go in the first round. Several others, including Georgia’s Sedrick Van Pran-Granger and Wisconsin’s Tanor Bortolini, are worth keeping an eye on later in the draft if the Steelers miss out on those top targets.

He grew up in the Steelers' backyard. Now he has the makings of a fan favorite

Wide receiver: Historically, the Steelers wait until the mid-to-late rounds to find a hidden gem at receiver. But the approach might change this season based on need and a new front office. By trading Diontae Johnson to the Carolina Panthers , the Steelers significantly changed the outlook at wide receiver. George Pickens , who is entering his third season, gives them a legit No. 1 whose big-play ability meshes well with Wilson’s patented deep ball. But behind him? The Steelers have candidates like third-year receiver Calvin Austin III and free-agent additions Quez Watkins , Van Jefferson and Cordarrelle Patterson . While those different body types and playing styles give the Steelers options, they would ideally come out of the draft with a new No. 2 receiver. Look for them to address this position around the second-to-fourth rounds.

Offensive tackle: Clearly, the Steelers were hoping to find an upgrade over left tackle Dan Moore Jr. when they moved up to draft Broderick Jones in the first round last season. Although Jones settled in at right tackle, Khan said at the combine that the 2023 first-round pick will eventually bump over to left tackle. That makes a right tackle another major need. This could be a decent year to find one, with 10 tackles ranked inside the top 63 players on Dane Brugler’s big board.

Defensive tackle:  The good news for the Steelers is that last year’s D-line investment paid off when Keeanu Benton began to blossom into an impactful player. The bad news? With Cameron Heyward fighting through injuries and Larry Ogunjobi enduring some inconsistencies, the Steelers still need to continue to infuse this position group with new, young talent. It’s time to start thinking about the future here.

Defensive back:  The Steelers’ secondary is in an interesting place. While safety Minkah Fitzpatrick is a perennial Pro Bowler and corner Joey Porter Jr. showed promise as a rookie, Pittsburgh could realistically upgrade at all of the other positions on the back end, including outside corner, nickel or even safety. Opposite Porter, Donte Jackson figues to be the leading candidate to start. Cory Trice Jr. , a seventh-round pick last year, is another option after losing his rookie season to a knee injury. Still, the fact that Jackson was almost cut by Carolina and Trice is a seventh-round pick coming off a major injury makes this an area the Steelers might want to address if the right player is available. It’s also worth noting many of the best corners are expected to go right around where the Steelers pick in the first round.

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The Athletic ’s latest mock drafts

April 24: Beat writer mock 3.0 When a potential top-10 pick goes sliding, Mark Kaboly scoops him up at No. 20.

April 22:   Steelers 7-round mock draft For the second consecutive year, Kaboly has the Steelers trading up for an offensive tackle in Round 1.

April 19:   NFL analytics mock draft Our projection model and consensus big board point to a high-ceiling offensive tackle for Pittsburgh.

April 17:   Dane Brugler’s 7-round mock draft Brugler picks all 259 picks, with the Steelers landing a center in Round 1, a receiver in Round 2 and an offensive tackle in Round 3.

April 15:  Mike DeFabo’s mock four different ways Mike DeFabo explores different approaches the Steelers could take to hit on all of their key needs.

April 8: Nick Baumgardner’s three-round mock Baumgardner has the Steelers adding two potential starters on the offensive line, plus contributors at wideout and cornerback.

April 4: Bruce Feldman’s Round 1 mock With insight from rival college coaches, Feldman has Pittsburgh landing an offensive tackle who once was projected as a top-five pick.

March 25: Round 1 mock: QBs go 1-2-3-4 When a top offensive tackle starts to slip, Ben Standig has the Steelers pouncing.

March 21: Beat writer mock draft 2.0 Kaboly lands the Steelers another Georgia offensive tackle in Round 1.

Steelers’ last five top picks

2023: OT Broderick Jones , No. 14 — As the first pick of the Khan era, Jones will always have a special place in Steelers lore. His addition into the lineup at right tackle in Week 9 helped jump-start a stagnant rushing attack. While Jones showed he has the athleticism and playing personality to succeed on the right side, his future remains at left tackle. In an ideal world, he’ll have a chance to bump over to the left side at some point this season.

2022: QB Kenny Pickett , No. 20 — A former Pitt quarterback playing NFL football in the same stadium where he starred in college should have been a feel-good story. Instead, it ended in disappointment. When Pickett failed to take a meaningful step forward in Year 2, the Steelers went out and got free-agent veteran Russell Wilson. The move irked Pickett, who asked for a trade, ending his tenure after just two seasons.

Steelers trade Kenny Pickett to Eagles: Source

2021: RB Najee Harris , No. 24 — In a league where running backs are often seen as replaceable, the Steelers took a big swing when they used their 2021 first-round pick on a ball carrier. Harris has the body type and running style the Steelers value. He’s proven to be a productive player late in the season. However, whether it’s Harris himself or the offensive line, the Steelers need to find more consistency running the ball in the first half of the year. A decision on his fifth-year option must be made by May 2.

2020: WR Chase Claypool , No. 49 — Claypool’s breakout four-touchdown game in 2020 feels like a lifetime ago. He went from a player full of promise and potential to a massive disappointment who became one of the most outspoken critics of the offense. His opinion of himself never matched his production. Somehow, Khan traded Claypool for the second-round pick (which became No. 32) that the team later used to draft Porter.

2019: LB Devin Bush , No. 10 — At one point, Bush was seen as the missing piece to complete the Steelers’ defense and the answer to their revolving door at inside linebacker. After just a few short seasons and a major knee injury, he’s now largely considered one of the biggest busts in Steelers history.

(Photo of Amarius Mims: Brandon Sloter / Image Of Sport / Getty Images)

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Mike DeFabo is a staff writer covering the Pittsburgh Steelers for The Athletic. Prior to joining The Athletic, he spent three years at the Pittsburgh Post-Gazette as a Penguins beat writer, three years covering Purdue football and basketball for CNHI and one season following the Michigan State basketball team to the 2019 Final Four for the Lansing State Journal. Mike is a native of Latrobe, Pennsylvania, the proud home of Steelers training camp, and graduated from American University in Washington, D.C. Follow Mike on Twitter @ MikeDeFabo

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Cardinals vs. Diamondbacks odds, line, score prediction, time: 2024 MLB picks, April 24 bets from proven model

Sportsline's model simulated wednesday's arizona diamondbacks vs. st. lous cardinals game 10,000 times and released its mlb picks, predictions and best bets.

The Arizona Diamondbacks face the St. Louis Cardinals in the third and final game of their series on Wednesday. Arizona knotted the series at 1-1 with a 14-1 thrashing of St. Louis on Tuesday, after losing 5-3 on Monday. The Diamondbacks (12-13), who have scored a Major League-high 148 runs on the season, have won five of 12 road games on the year. The Cardinals (10-14) will be looking to avoid their second series loss in a row, after being swept by Milwaukee this past weekend. St. Louis has won nine of the past 15 games against Arizona.

The game is slated to begin at 1:15 p.m. ET from Busch Stadium in St. Louis. The Diamondbacks have the seventh-best team batting average at .255, while the Cardinals are 26th at .219. St. Louis is a -114 favorite on the money line in the latest Diamondbacks vs. Cardinals odds from SportsLine consensus, while the over-under for total runs scored is 8. Before making any Cardinals vs. Diamondbacks picks, be sure to see the MLB predictions and betting advice from SportsLine's proven computer model .

The SportsLine Projection Model simulates every MLB game 10,000 times. It enters Week 5 of the 2024 MLB season on a profitable 110-85 run on top-rated MLB picks dating back to last season, and it has excelled on top-rated run-line picks during that span, going 19-7 (+737). Anybody following has seen huge returns.

Now, the model has set its sights on Cardinals vs. Diamondbacks and just locked in its picks and MLB predictions. You can head to SportsLine now to see the model's picks . Here are several  MLB odds  and betting lines for Diamondbacks vs. Cardinals:

• Diamondbacks vs. Cardinals money line: Arizona -105, St. Louis -114
• Diamondbacks vs. Cardinals over/under: 8 runs
• Diamondbacks vs. Cardinals run line: Arizona -1.5 (+157)
• ARIZ: The Diamondbacks have hit the money line in 37 of their last 63 games (+11.20 units)
• STL: The Cardinals have hit the game total under in 32 of their last 58 games (+5.80 units)
• Diamondbacks vs. Cardinals picks: See picks at SportsLine

Why you should back the Diamondbacks

Left fielder Lourdes Gurriel Jr. has been on fire. In Tuesday's big win, he was 3-for-5 with a double and an RBI. He went 3-for-4 with a double in a 7-3 loss at San Francisco on Saturday. Over the last five games, Gurriel is 10-for-24 (.417) with two doubles and two RBI. In 23 games this season, he is hitting .300 with five doubles, five home runs, 22 RBI and 18 runs scored.

Right fielder Jake McCarthy has hits in seven of the past eight games, including at least one in each of the last four. In Tuesday's win, he was 2-for-4 with a walk and a run scored. He was 2-for-3 with a double, one RBI and one run scored in a 5-0 win over St. Louis on April 14. In 21 games this season, McCarthy is batting .300 with three doubles, six RBI and 12 runs scored.  See which team to pick here . 

Why you should back the Cardinals

Right-hander Kyle Gibson will start for St. Louis. He has had two solid starts among his four on the season. In a 2-1 loss to the Milwaukee Brewers on Friday, he received a no-decision, going six innings and allowing three hits, one earned run, four walks and three strikeouts. In his first start of the season at San Diego on April 1, he picked up the win, pitching seven innings and allowing two earned runs, two walks and four strikeouts in a 6-2 win.

Shortstop Masyn Winn is off to a fast start to the season. In 22 games, he is batting .313 with three doubles, two triples, six RBI and eight runs scored. He has hits in six of his past 10 games, including a 2-for-3 performance with a double and a walk in a 3-1 win at Oakland on April 15. He was 2-for-4 with a triple, RBI and two runs scored in a 9-6 win at Arizona on April 12.  See which team to pick here . 

How to make Cardinals vs. Diamondbacks picks

SportsLine's model is leaning Over on the total, projecting 9.1 combined runs. It also says one side of the money line has all the value. You can only get the model's MLB picks at SportsLine .

So who wins Diamondbacks vs. Cardinals, and which side of the money line should you back? Visit SportsLine now to see which side you need to jump on, all from the model that's 110-85 on top-rated MLB picks , and find out.

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Detroit Lions NFL mock draft roundup: Predictions from all over on Round 1 pick

The 2024 NFL draft has finally arrived.

After months of attempted prognosticating over every individual pick for all 32 teams, the time to actually make the picks is here. 257 players will be selected over a three-day event happening in downtown Detroit at Campus Martius Park. 

The Detroit Lions have seven picks throughout the three days, barring any trades, starting with the 29th overall pick in the first round on Thursday. The Lions won the division for the first time in 30 years, won the first playoff game since 1991, made the NFC championship game and were a half of football from the first Super Bowl appearance in franchise history, pushing them into unfamiliar draft territory in the back half of the first round.

Now, the Lions will have to try to get over that hump after establishing themselves as one of the top teams in football, led by quarterback Jared Goff and a young core comprised of stars like Amon-Ra St. Brown , Penei Sewell, Jahmyr Gibbs, Sam LaPorta and Aidan Hutchinson. 

SUN GOD GOT PAID: Lions sign Amon-Ra St. Brown to mammoth 4-year contract extension

General manager Brad Holmes has had a successful draft record in his time in charge so far, finding the right types of players to fit into Dan Campbell’s team culture, but will have his first crack much later than previous years, barring a trade up.

The Lions have plenty of options available at 29 with their first pick , including not even making the selection at all, either trading up for a prospect they love or trading down for more draft picks. They could add to a strength offensively or address a hole on the defensive side. The beauty of the draft is in the unknown leading up to it and the shock when reality doesn’t match predictions.

With that being said, here is a complete list of what mock drafts are saying the Lions will do with the 29th pick: 

Free Press mock drafts

Lions beat writer Dave Birkett has done four mock drafts since the conclusion of the 2023 season, with three of them ending with Detroit securing an offensive lineman prospect, and one grabbing a defensive back. Here are the players Birkett has mocked to Detroit.

Mock draft 1.0 Feb. 22: Missouri CB Ennis Rakestraw Jr.

Mock draft 2.0 March 6: Oregon C Jackson Powers-Johnson.

Mock draft 3.0 March 20: Duke OL Graham Barton.

Mock draft 4.0 April18: West Virginia OL Zach Frazier.

Now, here are what other outlets are predicting the Lions will do. 

USA TODAY/Draftwire

Curt Popejoy April 18: Oregon C Jackson Powers-Johnson.

Alyssa Barbieri 4/22: Missouri DL Darius Robinson.

Nate Davis 4/19: Alabama DB Kool-Aid McKinstry.

Nate Davis 4/22: Lions trade out of first round in deal with Raiders so Las Vegas can select Washington QB Michael Penix Jr. 

Tyler Dragon 4/19: Michigan DL Kris Jenkins.

Draftwire 4/24: Illinois DL Jer’Zhan Newton.

Draftwire 4/19: Missouri DL Darius Robinson.

Detroit Lions

Tim Twentyman mock draft 4.0 4/24: Duke OL Graham Barton.

Tim Twentyman mock draft 3.0 4/1: Texas WR Adonai Mitchell.

Jordan Reid mock draft 4/22: Iowa DB Cooper DeJean.

Mel Kiper mock draft 4/10: Texas WR Adonai Mitchell.

Field Yates mock draft 4/3: Iowa DB Cooper DeJean.

Kiper + Yates mock draft 4/15: Missouri DL Darius Robinson.

ESPN beat writer mock 4/24: Alabama DB Kool-Aid McKinstry.

The Athletic

Dane Brugler’s seven-round mock 4/17: Lions trade back with Raiders for second and third round pick, add West Virginia OL Zach Frazier at 44 overall. Las Vegas selects Washington QB Michael Penix Jr.

32-Beat writer mock draft 4/24: Oregon C Jackson Powers-Johnson.

(note: All of CBS' mock drafts can be found at the same link.)

Ryan Wilson 4/23: Penn State EDGE Chop Robinson.

Chris Trapasso 4/23: Texas WR Adonai Mitchell.

Josh Edwards 4/22: Alabama DB Kool-Aid McKinstry.

Tom Fornelli 4/23: Alabama DB Kool-Aid McKinstry.

Kyle Stackpole 4/22: Missouri DB Ennis Rakestraw Jr.

Pete Prisco 4/22: Penn State EDGE Chop Robinson.

Rob Rang 4/23: Florida State WR Keon Coleman.

Nick Wright 4/22: Texas WR Xavier Worthy.

Carmen Vitali + David Helman 4/18: UCLA EDGE Laiatu Latu.

Pro Football Focus

32 analyst mock draft 4/22: Lions trade up to No. 15 for Toledo CB Quinyon Mitchell.

Nathan Jahnke 4/17: Lions trade back to No. 32 to select Alabama DB Kool-Aid McKinstry.

Sam Monson 4/15: Alabama DB Kool-Aid McKinstry.

Steve Palazzolo 4/10: Penn State EDGE Chop Robinson.

Max Chadwick 4/8: Alabama DB Kool-Aid McKinstry.

Yahoo! Sports

Tice + Charles McDonald 4/24: Ohio State DL Michael Hall Jr. 

Charles Davis 4/24: Alabama DB Kool-Aid McKinstry.

Bucky Brooks 4/23: Alabama DB Kool-Aid McKinstry.

Eric Edholm 4/22: West Virginia OL Zach Frazier.

Chad Reuter seven-round mock 4/19: Missouri DL Darius Robinson.

Dan Parr 4/18: Alabama DB Kool-Aid McKinstry.

Gennaro Filice 4/11 : Alabama DB Kool-Aid McKinstry.

Peter Schrager 4/16 : West Virginia OL Zach Frazier.

Connor Rogers, Kyle Dvorchark, Eric Froton 4/23: Missouri DL Darius Robinson.

Chris Simms 4/22: Penn State EDGE Chop Robinson.

Connor Rogers 4/17: Penn State EDGE Chop Robinson.

Mike Florio 4/23: Lions trade back with Raiders so Las Vegas can select Washington QB Michael Penix Jr. at 29. 

Washington Post

Jason La Canfora 4/24: Clemson DB Nate Wiggins.

Danny Kelly 4/24: Iowa DB Cooper DeJean.

Ben Solak 4/22: Alabama DB Kool-Aid McKinstry.

Danny Heifetz 4/23: Illinois DL Jer’Zhan Newton.

Alabama cornerback Kool-Aid McKinstry was by far the most popular prospect mocked to the Lions, landing in Detroit in 12 of the 50 mock drafts listed. Penn State pass rusher Chop Robinson was the second-most popular choice, appearing in five mock drafts. On top of bringing talent to two areas of presumed need on the Lions defense, McKinstry and Robinson also carry the pedigree of having an elite first name.

Offensive line was another popular choice with a plethora of options at the end of the first round. West Virginia center Zach Frazier was the most common pairing, appearing four times, followed by Oregon center Jackson Powers-Johnson with three and Duke guard Graham Barton twice. Wide receiver appeared five times, with three selections being Texas wideout Adonai Mitchell, also including his teammate Xavier Worthy and former MSU star Keon Coleman.

Carlos Monarrez: This Detroit Lions NFL draft move would get Brad Holmes an F from me

Three separate analysts with three different outlets all predicted the Lions would trade out of their pick in a deal with the Las Vegas Raiders, who would move back into the first round to draft Washington quarterback Michael Penix Jr. It is an interesting option to keep note of since Holmes said trading back is a real possibility.

Most drafts focused on the Lions' defense, with other popular prospects such as Iowa cornerback Cooper DeJean, Illinois defensive tackle Jer'Zhan Newton, Missouri defensive lineman and Canton native Darius Robinson and his teammate cornerback Ennis Rakestraw Jr.

As you can see, the options are endless for Holmes and the Lions at 29, so buckle up and prepare for a surprise Thursday night.

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Orioles vs. Angels odds, prediction: MLB picks, props for Tuesday

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Grayson Rodriguez has been among the best pitchers in the American League for what has quietly become a lengthy sample. 

Let’s take a look at why that matters in Tuesday’s matchup between the Orioles and Angels in Anaheim.

Orioles vs. Angels odds

Orioles vs. angels prediction.

Since being recalled by the Orioles last July, the right-handed Rodriguez has pitched to an ERA of 2.59 across 100 2/3 innings. He’s allowed an xFIP of only 3.65 in that span.

He has pitched to a 115 Stuff+ this season, and has located effectively as well with a 102 Location+ rating.

He has successfully worked a quality changeup and excellent curveball off of his high velocity fastball (averaging 96.2 mph this year) to good results. Batters own an xSLG of .254 and .212 off of those two pitches, respectively.

Rodriguez has managed to finish five or more innings in each of his four starts this season for a total of 24 innings, including a strong opening day showing against the Angels in which he picked up the win in six innings of work (4 hits, 1 ER, 9 Ks, 1 BB).

The Angels have hit to a wRC+ of only 92 versus righties this season. They have struck out 23.5% of the time, and they safely project to be a below-average side versus right-handed pitching moving forward.

There is a good chance we see Rodriguez backed by some early run support, as a high-powered Orioles offense matches up against Griffin Canning.

Canning’s strikeout percentage has trended down to 18.2% this season, which coincides with a lesser Stuff+ of 84.

He owns an xERA of 5.31, an xFIP of 4.89,and has allowed an xSLG of .797 with his fastball, which he throws 39.9% of the time.

The Orioles have hit to a wRC+ of 130 versus right-handed pitching this season, which is the second-best mark in MLB . They have struck out only 20% of the time versus righties, and own a league leading 35% hard-hit rate.

In splits to both sides, Baltimore owns a league-leading .485 xSLG rate this season. Their 5.73 runs scored per game ranks second in MLB.

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Orioles vs. Angels pick

Rodriguez is priced at +145 to record a win on bet365, and I see value with that number.

He has a great chance of posting a quality outing versus this Angels lineup, and should receive early run support as one of the league’s best offenses takes on Canning.

Pick: Grayson Rodriguez to record a win (+145, bet365 | Play to +135)

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NFL Draft 2024: Jets round 1 prediction | Marvin Harrison Jr., Rome Odunze, Brock Bowers?

• Updated: Apr. 24, 2024, 9:00 a.m. |
• Published: Apr. 24, 2024, 8:31 a.m.

New York Jets general manager Joe Douglas speaks during a press conference at the NFL football scouting combine in Indianapolis, Wednesday, Feb. 28, 2024. (AP Photo/Michael Conroy) AP

• Andy Vasquez | NJ Advance Media for NJ.com

The NFL Draft is just a day away, which means soon we’ll know what the Jets will do with their much-talked-about 10th overall pick.

Some think they must bolster their offensive line in front of quarterback Aaron Rodgers, and grab the best tackle available. Others believe a tight end, like Georgia’s Brock Bowers, has the versatility to break the mold and create nightmares as an offensive weapon in space.

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