How to Choose the Right Statistical Test for Your Research

Every week we meet researchers who collected good data and then got stuck at the same point: which statistical test do I run? The choice feels overwhelming, but in practice it comes down to three questions.

Question 1: What type of data do you have? Categorical data (gender, yes/no, grouped responses) and continuous data (age, scores, income) lead to different families of tests. Most confusion disappears once you classify each variable honestly.

Question 2: Are you comparing groups, or measuring relationships? Comparing men vs women on a score points to t-tests or ANOVA. Asking whether study hours relate to exam performance points to correlation or regression. Write your research question as a sentence — the verb usually tells you which family you're in.

Question 3: How many groups or variables are involved? Two groups → t-test. Three or more → ANOVA. One predictor → simple regression. Several predictors → multiple regression. Categorical outcome → chi-square or logistic regression.

Before running anything, check your assumptions: normality, sample size, and independence of observations. Violated assumptions don't always kill your analysis — but they change which version of the test (parametric vs non-parametric) you should use.

Finally, interpretation matters more than computation. Software will happily produce a p-value for the wrong test. If you can explain in plain language what your test compares and what the result means for your research question, you are ahead of most published papers.

If you're stuck, our research consulting service walks you through test selection, runs the analysis with you, and prepares interpretation you can defend — for theses, dissertations and publications.