essay about statistical significance

Statistics: Significance of Statistics

Introduction.

Statistics is a course that is advantageous and becomes ingrained in one’s life. The importance of statistical data is almost infinite, as demonstrated by confidence theory and science (Harris et al., 2017). Metrics are important because they provide accurate information about circumstances. However, several fields are abusing statisticians in various ways, such as excluding statistical courses from their curriculum structure. The following is a summary of the significance of statistics, as well as two real-life examples.

Significance of Statistics

The first incentive to study statistics is to become a more knowledgeable shopper. Metrics, as any other instrument, can be used or misused. Some people would deliberately lie and use survey results to mislead others. However, many well-intentioned people mistakenly announce erroneous statistical results. Individuals would be better positioned to analyze the information they have been given if they grasp some basic statistical principles.

Another purpose is to improve logical and analytical thinking abilities among people. Most high school and intermediate undergraduate students have a range of critical reasoning and analytic skills at their disposal. The study of statistical data will help students refine and grow these abilities (Harris et al., 2017). To succeed in statistics, one should cultivate and apply systematic critical thinking skills that are high-level and innovative.

Two Real-Life Examples

Teachers act as researchers in their classrooms, recognizing which educational techniques work best with which students and why. They also estimate test information to assess whether or not students are functioning as intended. There are statistical reports on student achievements at all stages of testing and education from kindergarten through graduation (Harris et al., 2017). Teachers, for example, may use new approaches to help students increase their grades by calculating the average of their scores. Students, conversely, can use statistical expectations of their scores for self-evaluation.

Quality Testing

Statistical approaches are used to assess, track, and maintain the overall quality of pharmaceutical goods in quality control statistical research. The value of medical specimens reporting high-quality patient outcomes cannot be overstated. Maintaining laboratory data quality is a never-ending task, and using statistical quality control methods thoughtfully is the key to success. Laboratories can ensure correct patient outcomes and continue to improve their performance using statistical methods.

Finally, since individual metrics can vary dramatically from day to day, the statistical analysis provides a solid mathematical basis for strategic decisions and adverse events. Facts and figures play an important role in improving our daily lives. Statistical data provide concrete data on efficiency and production, making them an ideal metric for determining quality and productivity. People may use statistics to learn about what has happened in the past and what can happen.

Harris, J. D., Brand, J. C., Cote, M. P., Faucett, S. C., & Dhawan, A. (2017). Research pearls: The significance of statistics and perils of pooling. Part 1: Clinical versus statistical significance. Arthroscopy: The Journal of Arthroscopic & Related Surgery , 33 (6), 1102–1112.

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Statistical Significance: A Thorough Guide

Published by Jamie Walker at August 25th, 2021 , Revised On August 3, 2023

When running an experiment, you might come across this term significant times, so we thought of comprising all the information you need to know about it. This blog sheds light on what it means for a result to be statistically significant and how to check that in research; let’s begin then.

What is Statistical Significance?

Statistical significance is described as the measure of the null hypothesis being plausible as compared to the acceptable level of vagueness regarding the true answer.

A null hypothesis , which you may remember, is a statistical theory suggesting that there exists no relationship in a set of variables.

In other words, the result of an experiment is considered to be statistically significant if it is not caused by chance for a given statistical significance level. The statistically significant level shows the risk tolerance and confidence level.

Now, what are both these terms?

The significance level in hypothesis testing is the probability or chance of making the wrong decision when the null hypothesis is plausible. It is denoted by the letter alpha.

While the confidence level is the probability that if a particular test or survey is repeated several times, the correct results can be obtained, it is denoted by 1-alpha.

If you conduct an A/B testing experiment with a significance level of, say, 95 per cent. It means that if you select a winner, you can be 95 per cent confident that the obtained outcomes are not an error caused by uncertainty. You can also say that there are 5 per cent chances of you being wrong.

Does this all make sense to you now?

Good! Let’s move forward, then.

Testing for Statistical Significance

When it comes to quantitative research , data can be assessed and evaluated via hypothesis testing or null hypothesis significance testing. This is a formal process for analyzing whether there is a statistically significant relationship between the variables or not.

Let’s recall null and alternative hypotheses before digging deeper.

• What is a null hypothesis?

A null hypothesis says there is no relationship between variables. It is denoted by H 0 .

• What is an alternative hypothesis?

This one predicts that there is an effect or relationship between variables. An alternative hypothesis is shown by the sign H a  or H 1.

Hypothesis testing always begins by assuming that the null hypothesis is plausible or true. With this method, you can evaluate and analyze the probability of getting your results under this prediction or assumption. Once done, you can either retain or reject the null hypothesis based on the results.

A statistically significant outcome not happening by chance depends on two key factors or variables:

Sample Size:

This refers to how small or big your sample is for a particular experiment. If the sample size is big, you can be more confident in the outcome of the study.

Effect Size:

Effect size is the size difference in results between the sample sets. If the effect size is small, you might need a huge sample size to check whether the difference is due to chance or is actually significant. On the contrary, if you observe a larger effect, you can validate it with a smaller sample size to a greater degree of confidence.

P values and Test Statistics

Two things are always produced by every statistical test :

• P-value – it tells you the chances of obtaining this result if the null hypothesis is plausible
• Test statistic -it indicates how close your data is related to the null hypothesis

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Why is Statistical Significance Significant in Research?

Statistical significance is of great value because it gives researchers a chance to confirm whether the findings they have at hand are reliable, real, and not something that occurred due to probability. However, the importance of statistical significance varies from researcher to researcher and the experiment they work on. The context in which a particular experiment is also conducted directly impacts the impact of statistical significance.

Statistical significance is crucial within academic research. It is mostly utilized in new pharmaceutical drug trials along with pathology studies in order to check vaccines. Moreover, the aim and objectives of academic research are to study and publish a series of scientific journals for which you need to make sure the results are statistically significant.

Outside of science and academics, statistical significance is, however, less important. Managers and business persons might use this to better comprehend and evaluate business strategies, but the fact that statistical significance is just quantifying how much trust to hold in research, people are least interested in this industry. They are undoubtedly more curious about the practical significance of a finding instead of statistical significance.

And this brings us to what practical significance is and how it is different from statistical significance.

Practical significance describes whether the research finding is significant enough to be meaningful in the actual world. It is shown by the effect size of the experiment or study.

So, the difference between these two is that statistical significance predicts that an effect exists in a study, while practical significance shows that the result is so big that it does not hold any impact in the real world.

This is all for this blog. If you have questions and requests, please feel free to contact our experts.

FAQs about Statistical Significance

1. what is statistical significance.

Statistical significance is described as the measure of the null hypothesis being plausible as compared to the acceptable level of uncertainty regarding the true answer. In other words, the result of an experiment is considered to be statistically significant if it is not caused by chance for a given statistical significance level. The statistically significant level shows the risk tolerance and confidence level.

2. What is the p-value?

The p-value tells you the chances of obtaining this result if the null hypothesis is plausible

3. What is a test statistic?

Test statistic indicates how close your data is related to the null hypothesis

4. Differentiate between statistical significance and practical significance.

The difference between these two is that statistical significance predicts that an effect exists in a study, while practical significance shows that the result is so big that it does not hold any impact in the real world.

5. What is the significance level?

6. what is the null and alternative hypothesis.

A null hypothesis says there is no relationship between variables. It is denoted by H0. An alternative hypothesis predicts that there is an effect or relationship between variables. It is showed by the sign Ha or H1.

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Statistics, Its Importance and Application Essay

Importance of statistics, examples of how statistics can be used.

Statistics is a science that helps businesses in decision-making. It entails the collection of data, tabulation, and inference making. In essence, Statistics is widely used in businesses to make forecasts, research on the market conditions, and ensure the quality of products. The importance of statistics is to determine the type of data required, how it is collected, and the way it is analyzed to get factual answers.

Statistics is the collection of numerical facts and figures on such things as population, education, economy, incomes, etc. Figures collected are referred to as data. The collection, analysis, and interpretation of data are referred to as statistical methods (Lind, Marchal, & Wathen, 2011).

Two subdivisions of the statistical method are:

• Descriptive statistics: Deals with compilation and presentation of data in various forms such as tables, graphs, and diagrams from which conclusions can be drawn and decisions made. Businesses, for example, use descriptive statistics when presenting their annual accounts and reports.
• Mathematical/inferential/inductive statistics: This deals with the tools of statistics. These are the techniques that are used to analyze, make estimates, inferences, and conclude the data collected (McClave, Benson, & Sincish, 2011).

Statistics have been collected since the earliest times in history. Rulers needed to have data on population and wealth so that taxes could be levied to maintain the state and the courts. Details on the composition of the population were necessary to determine the strength of the nation. With the growth of the population and the advent of the industrial revolution in the 18 th and 19 th centuries, there was a need for greater volumes of statistics in an increasing variety of subjects such as production, expenditure, incomes, imports, and exports. In the 19 th and 20 th centuries, governments worldwide took more control in economic activities such as education and health. This led to the enormous expansion of statistics collected by governments (Lind, Marchal, & Wathen, 2011).

The government’s economic activities have expanded in the last three centuries and so have the companies/businesses grown, as well. Indeed, some have grown to such an extent that their annual turnover is greater than the annual budgets of some governments. Big firms have to make decisions based on data. The companies collect data on their own other than these sources to establish:

• Competition
• Customer needs
• Production and personnel costs
• Accounting reports on liabilities, assets, losses, and income

The tools of statistics are important for companies in areas such as planning, forecasting, and quality control (McClave, Benson, & Sincish, 2011).

To Ensure Quality

A continuous check into quality using programs is very helpful in ensuring that only quality products come out of production firms. This, in turn, ensures that there is minimum wastage or errors in the production of goods and services (McClave, Benson, & Sincish, 2011).

Making Connections

Statistics are good in revealing relationships between variables – a good example is when a company makes a close relationship between the numbers of dissatisfied customers and the turnover. Indeed, there is an inverse relationship between the number of dissatisfied customers and turnover.

Backing Judgment

With only a small sample of the population studied, the management can come up with a concrete understanding of how the customers will relate to their products. This, therefore, will help them decide on whether to or not continue with that line of production (Lind, Marchal, & Wathen, 2011).

Lind, D., Marchal, G., & Wathen, A. (2011). Basic statistics for business and economics (7 th ed.). New York, NY: McGraw-Hill/Irwin.

McClave, T., Benson, G., & Sincish, T. (2011). Statistics for business and economics (11 th ed.). Boston, MA: Pearson-Prentice Hall.

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When Statistical Significance Is Not Enough: Investigating Relevance, Practical Significance, and Statistical Significance

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Statistical Significance – Everything You Need To Know

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In mathematical and scientific studies, statistical significance serves as a tool to help researchers assess whether the outcome of a study is a result of chance or depicts a true effect. For this, the p-value is commonly used to reject or confirm the null hypothesis. In the case of rejection, it can be determined that there is no effect between variables. If it is confirmed, it can be determined that a true effect or relationship is present, meaning the results are statistically significant. Learn more about statistical significance in this article.

Inhaltsverzeichnis

• 1 Statistical Significance – In a Nutshell
• 2 Definition: Statistical significanc
• 3 Testing for statistical significance
• 4 Statistical significance and significance level
• 5 The problem with statistical significance
• 6 Types of significance in research

Statistical Significance – In a Nutshell

• Statistical significance is the claim that a set of observed information or data is not the result of coincidence.
• Statistical significance is a probability measure of the likelihood of a study’s null hypothesis being correct.
• A high statistical significance shows that an observed correlation between the observed data is unlikely to be coincidental.
• There are a variety of types of significance tests that researchers can use as a measurement tool.

Definition: Statistical significanc

Statistical significance is a claim or determination made by a researcher that a group of observed data results from a particular cause instead of the product of chance or coincidence. The statistical significance can be described as strong or weak. When used in statistics , this concept is often expressed in terms of a p-value, which is a probability measurement used in observing data, given that the null hypothesis is true. The null hypothesis usually assumes that there is no relationship or effect between variables. If the p-value is less than the threshold (usually set at 0.05), the results are deemed statistically significant. This assesses that the observed data has a highly unlikely chance of occurring if the null hypothesis were true, thus providing evidence against the null hypothesis and in favor of an alternative hypothesis.

Testing for statistical significance

Testing for statistical significance is essential in quantitative research . Researchers conducting quantitative studies analyze their observed data through hypothesis testing . Therefore, statistical significance testing is the formal way of evaluating the correlation between variables or sets of data. The following presents guidelines for testing statistical significance:

Null and alternative hypotheses

The first step is categorizing the research predictions into null and alternative hypotheses . Hypothesis testing always begins by assuming that the null hypothesis is correct or justified. After assuming the null hypothesis is accurate, you can use hypothesis testing to assess the probability of obtaining your research results under this assumption. The outcome of your test will help you determine whether to reject or accept your null hypothesis.

You plan an experimental study to test if socializing can make you less productive. Start your experiment by stating your prediction into null or alternative hypothesis:

• Null hypothesis: No difference in productivity between socializing and not socializing
• Alternative hypothesis: Socializing fewer leads to more productivity than socializing more

Test statistics and proportional values (p-values)

All statistical tests produce a test statistic and p-value.

• A test statistic indicates how closely your observed data matches the formulated null hypothesis.
• The p-value indicates the probability of getting the study outcome if the null premise is correct.

A low p-value means that a result is not easily explainable by chance alone; hence the null hypothesis can be rejected. In contrast, a large p-value means that the result is explainable by chance alone, so you can retain the null hypothesis. Therefore, the p-value determines the statistical significance.

• You must begin by collecting data from the experiment and control group .
• The experiment group includes subjects that socialize a lot
• The control group does both (socializing and not socializing).
• Next, record the productivity ratings for both groups on a scale from 1-5.
• Then perform a  t-test to determine whether actively socializing leads to less productivity.
• The test statistic (t value) to help you determine how much the sample differs from your formulated null hypothesis.
• The p-value to show the likelihood of the results showing if the null premise is right
• Compare the p-value to your assumed significance level for hypothesis test results.

Statistical significance and significance level

The significance level is a value set by a researcher before the experiment as the brink for statistical significance. The significance level is the extreme jeopardy of making a false optimistic inference that you are prepared to take. The significance level measures the strength of the evidence that must be present in your sample before you decide to reject or accept the null hypothesis.

A hypothesis test always ends by comparing the p value to the significance level. This helps you determine whether to retain or castoff the null hypothesis.

• If the p-value is greater than the significance level , it means that the null supposition is not disproved and the results of the study are not statistically noteworthy
• If the proportional value is smaller than the significance level, the outcomes are statistically significant and are construed as rebutting the null hypothesis.

Most researchers set the significance level at 5%. A 0.05 significance level indicates a 5% risk of concluding that a difference exists when no difference exists. It is worth mentioning that hypothesis testing only shows you whether to castoff or maintain the null hypothesis in favour of the alternate hypothesis.

Your hypothesis test gives you a proportional value of 0.00029. This value is less than the predetermined significance level of 0.04. So, you can deliberate your outcomes as statistically significant. Therefore, you can cast off the null hypothesis. This means that the difference in productivity level can be accredited to the tentative influences.

The problem with statistical significance

The main problem with statistical significance is that it is oftentimes categorized as statistically significant or not based on conformist thresholds that lack theoretical backup. This implies that a slight decrease in the p-value can alter the findings from insignificant to significant, even if there is no noteworthy change in the effect.

Furthermore, statistical significance can be misleading when used independently. This is because the sample size affects it. For instance, in large samples, the probability of obtaining statistically significant results is high even whether the effects are minimal or not noteworthy in real word contexts.

Types of significance in research

Apart from statistical significance, you can use the following to predict research outcomes:

• Practical significance determines if the research results are vital enough to be valuable in the real world. Therefore, it is indicated by the study’s effect size .

Calculate the impact size of your study’s statistically significant findings in the experiment group. The Cohen’s d of this result is 0.266 , which indicates a minimal impact size.

Clinical significance is preferred for intervention and treatment research. Treatments are marked clinically significant when they tangibly improve patients’ lives.

What is statistical significance?

Statistical significance is the claim that a set of observed information or data is not the result of coincidence but can be credited to a particular cause.

What does statistical significance measure?

Statistical significance measures the likelihood of a study’s null hypothesis being correct, likened to the acceptable level of ambiguity concerning the correct answer.

How are statistical significance and significance level related?

A hypothesis test always ends by comparing the p value to the significance level.

What is the p-value?

The p value measures the likelihood that an observed difference could have occurred by coincidence. It determines a result’s statistical significance.

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ESSAY WRITING SAMPLE: STATISTICAL SIGNIFICANCE

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T he Difference between Statistical Significance and the True Importance (Clinical Significance) Of the Relationship between Variables or the Degree of Difference between Groups Affect Your Practice Decision Making

The true significance of the relationship between the variables determines the clinical relevance of a study that includes significance, validity, confidence, and effect. It is important to understand these research aspects now that they help researchers to better use the evidence in improving their decision-making skills.   [“ Write my essay for me ?” Get help here.]

Tests that concern statistical significance and the effect sizes are in most cases invoked to offer the desired findings. The problem is that statistically significance outcomes can only be used to determine if the null hypothesis should be rejected at some point of certainty and this follows the assumption that certain conditions, most significantly, random sampling from a population that has been well defined, have been satisfied.  [“ Write my essay for me ?” Get help here.]

For example, the psycho-educational approach used in the Social Work Research: Measuring Group Success case study, the subject of the study were able to increase their knowledge of the course in such topics like survival skills, crisis, healthy support systems, healing sexually, and building healthy relationships. Such knowledge was helpful to the researcher as they offer guidelines for social workers when dealing with stressful situations (Plummer, Makris, & Brocksen, 2014).

References Plummer, S.-B., Makris, S., & Brocksen S. M. (Eds.). (2014).  Social work case studies: Foundation year . Baltimore, MD: Laureate International Universities Publishing

Yegidis, B. L., Weinbach, R. W., & Myers, L. L . (2012). Research methods for social workers. Upper Saddle River, NJ: Allyn & Bacon

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• Logic of Statistical Significance

Logic of Statistical Significance - Essay Example

• Subject: Statistics
• Type: Essay
• Level: Ph.D.
• Pages: 2 (500 words)
• Downloads: 4
• Author: ghickle

Extract of sample "Logic of Statistical Significance"

Logic of statistical significance DateLogic of statistical significanceThe term “significance level” has always been misleading to many researchers as they do not fully understand it (StatPac Inc, 2012). A research finding is said to be statistically significant if its statistics can be relied on. In statistics, a research finding is said to be significant if it has a high probability of being true. Significance levels depict the likelihood of a result being true (Creative Research Systems, 2012).

For example, suppose a survey is undertaken to determine if there exists a difference in preferences with respect to gender in the use of cell phones. Taking a sample of 1000 people, the nominal survey data collected is as shown:PreferencesTotalGenderVideoSoundNeithermale20015050400female25030050600total4504501001000Suppose the null hypothesis is gender and preferences are independent. Then on evaluating the test statistics, getting a value of 16.2 using the degree of freedom of 2. Comparing this t-statistic with the critical value obtained from the chi- distribution table, suppose the significance level of 0.

05 is chosen. Then it can be ascertained that the finding is significant as the t-statistic is higher than the critical value. This leads to the rejection of the null hypothesis and also the arrival of a conclusion that there exists a relationship between preferences and gender for that product.For the case of ordinal data survey, as in the example of a survey comparing the mean weights of male and female students. A statistical hypothesis test is used for making decisions on the data. The test result is calculated from the null hypothesis.

The test sample is said to be statistically significant if its occurrence is unlikely to have been by chance alone. The statistically significant result that is given by probability p-value is less than the threshold of a significant level then it justifies the rejection of the null hypothesis. Once the variations have been attained, the Fischer value is calculated and is compared to the f critical value from the table at a given degree of confidence (Carlson, 1976).Another important concept to consideration is the use of one-tailed or two-tailed significance tests.

(StatPac Inc, 2012) The hypothesis determines the selection of each. If the hypothesis gives directions, for example, men generally weigh more than women then the one-tailed significance test is employed. However, if the hypothesis gives no directions as in the example, there is no significant difference in performances between boys and girls, and then the two-tailed significance test is used. The two-test probability is exactly twice the one-test probability therefore; it is actually safe to use it.

However, there are cases where the one-test is important. WORK CITEDCarlson, R. (1976). The Logic of Tests of Significance. Philosophy of Science , 116-128.Creative Research Systems. (2012). Significance in Statistics & Surveys. Retrieved October 14, 2012, from http://www.surveysystem.com/signif.htmStatPac Inc. (2012). Statistical Significance. Retrieved October 14, 2012, from StatPac: http://www.statpac.com/surveys/statistical-significance.htm

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