Answer a few simple questions about your research goal and your data, and get the right statistical test — complete with assumptions, an APA reporting example, and the effect size to use.
Choosing a statistical test is not guesswork — it is a short sequence of decisions that follows directly from your research design. Once you can answer four questions about your study, the correct test usually identifies itself. This selector walks you through exactly those questions, in the same order a statistician would ask them.
Every quantitative hypothesis falls into a handful of families. Are you comparing groups (do men and women differ in anxiety scores?), testing a relationship (is study time associated with exam performance?), predicting an outcome (do age, motivation, and attendance predict final grades?), or checking the reliability of a questionnaire scale? Each family has its own set of tests, so this single question eliminates most of the options immediately.
The measurement level of your dependent variable determines the branch of tests available to you. Continuous variables (scores, weight, reaction time) open the door to t-tests, ANOVA, Pearson correlation, and linear regression. Categorical variables (yes/no, diagnostic groups) point toward chi-square tests and logistic regression. Ordinal data, such as single Likert items or rankings, are typically handled with rank-based methods like Mann-Whitney U or Spearman correlation.
Two groups call for a t-test; three or more call for ANOVA — running multiple t-tests instead inflates your false-positive rate. Just as important is whether your samples are independent (different participants in each group) or paired (the same participants measured twice). Independent and paired designs use different tests, and mixing them up produces unreliable p-values.
Parametric tests such as the t-test and ANOVA assume approximately normally distributed data. When normality fails — check it with a Shapiro-Wilk test — you switch to the nonparametric alternative: Mann-Whitney U instead of the independent t-test, Wilcoxon signed-rank instead of the paired t-test, and Kruskal-Wallis instead of one-way ANOVA. You lose a little statistical power, but your results remain defensible.
Want the full decision process with worked examples? Read our complete guide on which statistical test to use and when, or let the quiz above do the thinking for you — then run the analysis instantly in our free Academic Stats Agent.
Calculate the exact sample size needed for your study design.
Calculate Cohen's d, eta-squared, Cramer's V, or correlation r.
Get a plain-English explanation of your p-value with APA formatting.
Generate perfectly formatted APA 7th edition results sentences.
Upload your data to the Academic Stats Agent and get the full analysis — assumption checks, test results, effect sizes, and APA-formatted tables — without touching SPSS. Or let our expert statisticians handle everything for you.