# Statistical Significance: A Thorough Guide

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at August 25th, 2021 , Revised On January 11, 2023When running an experiment, you might come across this term significant times, so we thought of comprising all the information you need to know on 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 could be obtained. It is denoted by 1-alpha.

If you conduct an A/B testing experiment with a significance level of, say, 95 percent. It means that if you select a winner, you can be 95 percent confident that the obtained outcomes are not an error caused by uncertainty. You can also say that there are 5 percent 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 showed by the sign H_{a} or H_{1.}

A hypothesis testing always begins with 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 for checking 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:

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

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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

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.

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

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

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.

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.

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.