Which Statistical Test Should I Use?
Answer a few simple questions about your data and research goal. Our decision tree covers 20 statistical tests — from t-tests and ANOVA to chi-square, correlation, and non-parametric alternatives.
Frequently Asked Questions About Choosing a Statistical Test
How do I know which statistical test to use?
Choosing the right statistical test depends on your research goal (comparing groups, examining relationships, or describing data), the number of groups, data type (continuous or categorical), and whether your data is normally distributed. StatMate's decision tree asks 4 simple questions to recommend the best test from 20 options.
What statistical test should I use for two groups?
For comparing means of two independent groups, use an independent-samples t-test (if data is normal) or Mann-Whitney U test (if non-normal). For the same participants measured twice (e.g., pre/post), use a paired-samples t-test or Wilcoxon signed-rank test.
What is the difference between a t-test and ANOVA?
A t-test compares means of 2 groups, while ANOVA (Analysis of Variance) compares means of 3 or more groups. Use a t-test for 2 groups and ANOVA for 3+. If ANOVA is significant, follow up with post-hoc tests to identify which groups differ.
Which test should I use for categorical data?
For testing associations between categorical variables (counts/frequencies), use the chi-square test. If any expected cell count is below 5, use Fisher's exact test instead. For paired binary data (before/after), use the McNemar test.
How do I choose between parametric and non-parametric tests?
Use parametric tests (t-test, ANOVA) when your data is approximately normally distributed and your sample is large enough (N > 30). Use non-parametric alternatives (Mann-Whitney, Kruskal-Wallis, Friedman) when data is skewed, ordinal, or your sample is small.
What is the difference between correlation and regression?
Correlation measures the strength and direction of the relationship between two variables, while regression creates a predictive model from one variable to another. Use correlation to check if a relationship exists; use regression when you want to predict outcomes.
How to Choose the Right Statistical Test: A Complete Guide
Choosing the right statistical test is one of the most common challenges in data analysis. The correct test depends on your research question, the type of data you have, and the number of groups or variables involved. Using the wrong test can lead to invalid conclusions — either false positives (Type I error) or missed real effects (Type II error). Our interactive decision tree simplifies this process by guiding you through a series of straightforward questions about your data.
20 Statistical Tests Covered in Our Decision Tree
StatMate's decision tree covers a comprehensive range of statistical tests: Independent and Paired t-Tests for two-group comparisons, One-Way ANOVA and Two-Way ANOVA for multi-group designs, Kruskal-Wallis and Friedman tests as non-parametric alternatives, Repeated Measures ANOVA for within-subjects designs, Chi-Square and Fisher's Exact Test for categorical data, McNemar Test for paired binary outcomes, Correlation and Regression for relationship analysis, plus Descriptive Statistics and Sample Size calculators. Each recommendation links directly to a free, interactive calculator with APA-formatted results.
How the Statistical Test Selection Flowchart Works
Start by selecting your research goal: comparing groups, examining relationships, describing data, or planning a study. The flowchart then asks about the number of groups, whether measurements are independent or paired, your data type (continuous vs. categorical), and whether normality assumptions are met. In just 2-4 clicks, you get a specific test recommendation with a direct link to run the analysis — no software installation required.