Most researchers do not choose a statistical test. They inherit one. A supervisor used a t-test, so the next student uses a t-test, and the one after that. It works often enough that nobody asks why, until a reviewer does. The reassuring part is that choosing the right test is not a feat of memory. It follows from three plain questions about your data, asked in order. Answer them and the test almost always picks itself.

1Start with your data, not the test

The mistake is to start from the test you already know and look for a way to apply it. Start from the other end. What kind of outcome did you measure, how many groups are you comparing, and does your data behave the way the test expects. Those three questions, in that order, lead you to the right test for almost every common study. Figure 1 is the map they lead to.

Match the test to your outcome CONTINUOUS · a number Two groups t-test or Mann-Whitney Three or more groups ANOVA or Kruskal-Wallis A relationship correlation or regression CATEGORICAL · a label Compare proportions Chi-square test Small numbers Fisher exact test Predict or adjust logistic regression TIME-TO-EVENT Describe survival Kaplan-Meier curve Compare groups log-rank test Adjust for covariates Cox regression Three families of outcome, three families of tests. Everything starts with knowing which kind you have.
Figure 1. The whole decision in one view. Find your outcome type along the top, then the row that matches what you are asking. Most of this guide is simply how to read this map for your own study.

2Question one: what kind of outcome did you measure?

Everything starts with the type of your outcome, the thing you are actually comparing.

Each type points to a different family of tests, the three columns of Figure 1. Get this one wrong and nothing downstream is right. A survival outcome forced into a t-test, for example, throws away the timing and the censoring that make it a survival outcome at all.

3Question two: how many groups, and are they linked?

For a continuous outcome, the next question is how many groups you compare and whether they are independent or paired.

Comparing two groups on a number? Read off the cell. Roughly normal Skewed or small Independent groups treatment vs control Student t-test Mann-Whitney U Paired measurements before vs after, same people Paired t-test Wilcoxon signed-rank Three or more groups follow the same logic, ANOVA when normal, Kruskal-Wallis when not.
Figure 2. The most common decision in clinical research. Two questions, independent or paired, and normal or skewed, fix the test for two-group comparisons. The pairing question is the one authors get wrong most often.

4Question three: does your data meet the test's assumptions?

The familiar tests, the t-test, ANOVA and Pearson correlation, are called parametric. They assume your data follow roughly a normal distribution, the symmetric bell shape. When the data are clearly skewed, or the sample is small and you cannot tell, the safer choice is a non-parametric test. Mann-Whitney replaces the t-test, Wilcoxon replaces the paired t-test, Kruskal-Wallis replaces ANOVA. These ask less of your data and rarely cost you much. Figure 3 shows the difference that decides it.

Symmetric (normal) mean = median use the t-test Skewed (long tail) median mean pulled right use Mann-Whitney
Figure 3. When a distribution is symmetric, the mean describes it well and the t-test fits. When it has a long tail, a few extreme values drag the mean away from the typical case, and a rank-based test like Mann-Whitney is the honest choice.

5When you are looking at a relationship, not a difference

Sometimes you are not comparing groups at all. You want to know whether two things move together, or whether one predicts another. For two continuous variables that rise and fall together, use correlation, Pearson when both are roughly normal and Spearman when they are not. When you want to predict an outcome from several variables at once, and to adjust for confounders, you need regression. Linear regression handles a continuous outcome, logistic regression a yes-or-no outcome, and Cox regression a time-to-event outcome. Regression is also how you answer the question reviewers ask most, whether your effect still holds after accounting for age, stage and the other usual suspects.

6The mistakes that cost you a reviewer's trust

A handful of errors come up again and again, and a reviewer spots every one of them in seconds. None is exotic, and all are avoidable once you have answered the three questions honestly. Figure 4 is the short list to hold your own analysis against before you submit.

Five test-choice errors reviewers catch Running many tests and reporting only the one that reached significance Treating paired data as independent, or independent data as paired Using a t-test on clearly skewed data instead of a rank-based test Comparing three or more groups with repeated two-group t-tests Reporting a result without naming the test or checking its assumptions
Figure 4. Every item here is a test-selection error, not an analysis that is merely hard. Each is visible from the methods section alone, which is exactly why reviewers find them so fast.

Choosing a test is not the hard part of research, but choosing the wrong one quietly undermines everything built on top of it. Start from your data, answer the three questions in order, and the choice is usually obvious. When it is not, that is the moment to ask someone before you run the analysis, not after a reviewer has sent it back.