Calculator Suite

Statistics guide

Which Statistical Test Should I Use?

Choose between descriptive statistics, t-tests, correlation, regression, and chi-square tests based on variable type and research question.

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Start with the question

If you want to summarize one numeric variable, start with descriptive statistics.

If you want to compare means, consider a t-test. If you want association between numeric variables, consider correlation or regression. If you want categorical count differences, consider chi-square.

Match the test to variable type

Numeric outcomes support means, standard deviations, t-tests, correlation, and regression when assumptions fit.

Categorical counts support chi-square tests when expected cell counts are large enough.

Check assumptions before p-values

Independence, sampling method, distribution shape, outliers, and expected counts can matter more than the final p-value.

A statistically significant result can still be practically small or caused by biased data collection.

Quick selection examples
The right test follows from the question, variable type, design, and assumptions.
  • Compare average delivery times for two independent teams: independent two-sample t-test.
  • Measure relationship between advertising spend and sales: correlation or linear regression.
  • Compare observed counts across product categories against expected counts: chi-square goodness-of-fit test.
Sources and disclaimer
NIST/SEMATECH e-Handbook: statistical methods

This guide is educational statistical context. Review study design and assumptions before using results for decisions.

Last updated: 2026-06-06. Reviewed by Calculator Suite editorial review.