How do you report the significance of a t-test in your research?

2 Report Structure

When reporting the results of a t-test, structure your findings in a clear and logical manner. Begin by stating the purpose of the test and the hypotheses you were testing. Then, present the test results, including the t-value and degrees of freedom (df), which is the number of individual scores that can vary without changing the sample mean. Follow this by reporting the p-value and whether this suggests a rejection of the null hypothesis, which posits no effect or no difference.

3 Significance Levels

The significance level of a t-test, often denoted as alpha (α), is the threshold at which you decide whether or not to reject the null hypothesis. Commonly set at 0.05, this level indicates a 5% risk of concluding that a difference exists when there is no actual difference. Report whether your test's p-value falls below or above this significance level, and explain what that means for your hypothesis.

4 Interpret Results

Interpreting the results is just as important as the numbers themselves. Explain what the statistical significance means in the context of your study. If the p-value is low, it suggests that the observed differences in means are unlikely due to random chance. However, statistical significance does not necessarily imply practical significance, so discuss whether the size of the difference is meaningful in real-world terms.

5 Visual Aids

Visual aids can enhance the comprehensibility of your t-test results. Consider including graphs such as box plots or bar charts that illustrate the distribution of your data and highlight the differences between groups. Ensure that these visuals are clearly labeled and include all necessary information, like means and confidence intervals, to help your audience understand the results at a glance.

6 Reporting Ethics

Finally, it's essential to approach reporting with integrity and transparency. Avoid cherry-picking data or misrepresenting the significance level to make your results appear more conclusive than they are. Acknowledge any limitations in your study that may affect the interpretation of the t-test, such as small sample sizes or potential biases, and be honest about what your findings suggest about your research question.

7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?