Statistical Test Chooser

Answer a few study-design questions to get a transparent test recommendation, a common alternative, and the reasoning path; the chooser does not analyze your data or certify assumptions.

Goal

Outcome / variable type

Number of groups / conditions

Related / paired observations?

Approximately normal?

Equal variances justified?

—Recommended starting point

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This is a transparent decision aid, not an automatic assumption checker or a substitute for study-design expertise. Normality usually concerns model residuals/differences rather than blindly testing every raw variable, and sparse categorical tables may need exact methods.

Core describe → test → interpret workflows

Use descriptive context first, choose the method that matches the design, then keep inputs, settings and result evidence reproducible instead of copying a p-value without its analysis recipe.

Start: Statistics Calculator Choose a statistical test

All 51 statistics tools

T-Test CalculatorOne-sample, Welch and paired mean tests Box Plot MakerQuartiles, spread, outliers and export Linear RegressionLine of best fit, R² and residuals Probability CalculatorEvents, odds and repeated trials CorrelationPearson, Spearman and paired association Standard ErrorSampling uncertainty and CI context One-Way ANOVACompare 3+ independent group means Chi-SquareCounts, independence and goodness of fit

Interpret statistical results in context

Use plots and descriptive summaries before formal tests. Check the sampling/design assumptions that matter for the chosen method, report effect size and uncertainty where available, and avoid treating a threshold such as p < .05 as proof of importance, causation, or truth. Browser calculations are educational/planning utilities, not domain-specific professional advice.