How to interpret statistical results

Are you tired of getting lost in a sea of numbers and statistical jargon? Fear not! In this article, we will break down the steps to interpreting statistical results like a pro.

Step 1: Understand the question and hypotheses

Before diving into the statistical analysis, it is crucial to have a clear understanding of the research question and hypotheses. What is the main question being asked? What are the null and alternative hypotheses?

For example, if the research question is, "Does caffeine improve athletic performance?", the null hypothesis would be, "Caffeine does not improve athletic performance", and the alternative hypothesis would be, "Caffeine does improve athletic performance".

Step 2: Determine the appropriate statistical test

Once the research question and hypotheses are clear, the next step is to determine the appropriate statistical test to use. This depends on the type of data being analyzed and the research question.

For example, if the research question is comparing the means of two groups, a t-test would be appropriate. If the research question is comparing more than two groups, analysis of variance (ANOVA) may be a suitable test. If the research question involves associations between variables, regression analysis could be used.

Step 3: Check assumptions

Before conducting the analysis, it is important to check the assumptions of the statistical test being used. Different statistical tests have different assumptions, and violating these assumptions can lead to misleading results.

For example, if conducting a t-test, assumptions include normality of the data and equal variances between groups. Violations of these assumptions could result in incorrect conclusions.

Step 4: Calculate the results

Now it's time to conduct the statistical analysis and calculate the results. This involves running the appropriate statistical test and obtaining the output.

For example, if using a t-test, the output will include the t-value, degrees of freedom, and the p-value.

Step 5: Interpret the results

Interpreting the statistical results can be the most challenging part of the analysis. It requires understanding what the output means in terms of the research question and hypotheses.

The p-value is often used to determine statistical significance. If the p-value is less than the pre-determined alpha level (typically 0.05), then the null hypothesis can be rejected in favor of the alternative hypothesis.

It is important to remember that statistical significance does not necessarily mean practical significance. Just because there is a statistically significant difference between groups does not mean it is meaningful or important in real life.

Step 6: Draw conclusions and implications

The final step in interpreting statistical results is to draw conclusions and implications based on the analysis. This involves considering the limitations of the study, potential confounding variables, and the overall implications of the findings.

For example, if the research question was whether caffeine improves athletic performance and the results showed a statistically significant difference between the caffeine and placebo groups, the conclusion could be that caffeine does improve athletic performance. However, it is important to consider factors such as dosage, individual responses to caffeine, and potential side effects.


Interpreting statistical results can be a daunting task, but by following these steps, you can gain confidence in analyzing and interpreting data. Remember to always consider the research question and hypotheses, determine the appropriate statistical test, check assumptions, calculate the results, interpret the results, and draw conclusions and implications. With practice, interpreting statistical results will become second nature. Happy analyzing!

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