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Running chi-square test of independence analysis...
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Analyze another fileTests whether two categorical variables are statistically independent. If your survey shows that product preference differs by age group, the chi-square test tells you if that pattern is real or just chance.
Use this when you have two categorical variables and want to test if they're related.
If you're comparing numeric means (not category counts), use T-Test or ANOVA.
Built for: Market researcher, survey analyst, social scientist, product manager
Typical data source: Survey data or categorical data with two columns to test for independence
Survey or categorical data
Minimum 30 rows · Best with 100-5000 responses
Tests whether two categorical variables are statistically independent using Pearson's chi-square test. Computes effect sizes (Cramer's V), standardized residuals, and visualizes associations with contingency heatmaps and grouped bar charts.
Test statistics, p-values, and effect sizes for all variable pairs
Observed frequencies in the contingency table
Observed vs expected frequencies
Cells driving the association (|residual| > 2 indicates significant contribution)
Cramer's V for all tested variable pairs
Proportional distribution across groups
Plain-English interpretation — what the numbers mean, what's significant, and what to do next.
Need something simpler? Categorical Analysis — Need broader categorical analysis beyond independence testing
Need more power? Logistic — Want to predict one category from others
Similar: Anova
See our FAQ for details on pricing, data privacy, and how the analysis works. Every report includes a Methodology section showing the statistical test, assumptions checked, and diagnostics run.
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