How to Choose the Right Statistical Test

Choosing the wrong statistical test is one of the most common reasons theses receive major revisions. The good news is that test selection follows a logical process. If you can answer four questions about your study, you can narrow the options down to one or two appropriate tests.

1.Question 1: What are you trying to find out?

• Differences between groups (e.g. do men and women differ in anxiety?)
• Relationships between variables (e.g. is sleep quality related to burnout?)
• Prediction (e.g. which factors predict readmission?)
• Agreement or reliability (e.g. do two raters agree?)

2.Question 2: What type are your variables?

Categorical variables place participants into groups (gender, diagnosis, yes/no). Ordinal variables have a meaningful order but unequal intervals (education level, single Likert items). Continuous variables are measured on a scale with equal intervals (age, blood pressure, total scale scores). The type of your outcome variable is the single most important factor in choosing a test.

3.Question 3: How many groups, and are they independent?

Independent groups contain different people (patients vs. controls). Dependent or paired groups contain the same people measured more than once (before and after an intervention) or matched pairs. Tests for independent and paired data are different, and mixing them up is a frequent error.

4.Question 4: Are parametric assumptions met?

Parametric tests assume a continuous outcome, approximately normal distribution within groups and, for many tests, similar variances. When these assumptions are clearly violated or samples are small, non-parametric alternatives are safer.

5.Quick reference guide

Comparing groups (continuous outcome)

• 2 independent groups: independent t-test / Mann–Whitney U
• 2 paired measurements: paired t-test / Wilcoxon signed-rank
• 3+ independent groups: one-way ANOVA / Kruskal–Wallis
• 3+ repeated measurements: repeated-measures ANOVA / Friedman
• Two factors at once: two-way ANOVA
• Controlling for a covariate: ANCOVA

Categorical outcome

• Association between two categorical variables: chi-square test
• Small expected counts: Fisher’s exact test
• Paired categorical data: McNemar test

Relationships and prediction

• Two continuous variables: Pearson correlation / Spearman rho
• Predicting a continuous outcome: multiple linear regression
• Predicting a binary outcome: binary logistic regression
• Time-to-event outcome: Kaplan–Meier and Cox regression
• Latent variables and complex models: structural equation modelling

6.Don’t forget the follow-up steps

A significant ANOVA only tells you that at least one group differs; post-hoc tests (Tukey, Bonferroni, Games–Howell) tell you which. Regression requires checks for multicollinearity, outliers and residuals. Always report effect sizes: Cohen’s d, eta squared, odds ratios or R squared.