Understanding Your Research Question

The first step in choosing the right statistical test is understanding the nature of your research question. Are you comparing groups, measuring associations, or looking at differences over time? For example, if you're comparing the average blood pressure between two groups (treatment vs. control), you're dealing with a comparison of means, which may require a t-test.

Type of Data You Have

The choice of test depends heavily on the type of data: categorical (e.g., gender, presence/absence of disease) or continuous (e.g., age, weight, cholesterol level). For instance, if you're examining whether smoking status (yes/no) is related to lung cancer incidence (yes/no), both variables are categorical—pointing you toward a Chi-Square test.

Number of Groups Being Compared

If you're comparing just two groups, a t-test might be appropriate. But if you're comparing more than two groups, such as average cholesterol levels among people with low, medium, and high physical activity levels, you'd use an ANOVA (Analysis of Variance). ANOVA tells you if there’s a difference but doesn’t specify where—it’s often followed by post-hoc tests.

Relationship vs. Difference

Decide whether you’re testing for a relationship (association) or a difference between groups. For example, if you’re exploring whether age correlates with systolic blood pressure, a Pearson correlation or linear regression is appropriate. But if you want to compare male and female mean hemoglobin levels, a t-test is your go-to.

Data Distribution Assumptions

Statistical tests like the t-test and ANOVA assume that your data is normally distributed and that variances are equal. If these assumptions are not met, non-parametric alternatives such as the Mann-Whitney U test or Kruskal-Wallis test may be more appropriate. For example, comparing median recovery times between two treatments with skewed data would call for a Mann-Whitney test.

Sample Size Consideration

Some tests are more robust with larger sample sizes. For instance, Chi-Square tests may give misleading results with small sample sizes, so Fisher’s Exact Test is preferred when cell counts are low. For example, if only 20 patients are involved and you want to test the association between two categorical variables like infection status and prior antibiotic use, Fisher’s Exact Test would be suitable.

Summary

Choosing the right statistical test isn’t about memorization—it’s about aligning your test with the type of data, number of groups, and your research goal. Think of your hypothesis as a question, and the statistical test as your answer tool. When in doubt, start with the basics, and consult a statistician or software output for guidance.