Upload a CSV, pick two categorical columns, and get the full association analysis — contingency heatmap, chi-square and Fisher exact tests, Cramér's V strength, and the exact combinations driving the relationship. Free.
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Free analyses run on up to 10,000 rows. Larger files are randomly sampled to that size — sign up to analyze your full dataset.
Cross-tabulating and testing for association...
Sent to — contingency heatmap, chi-square + Fisher exact results, Cramér's V, largest deviations, R code, and AI insights.
Analyze another fileThe analysis cross-tabulates your two columns and asks whether the observed counts could plausibly arise if the columns were unrelated. The chi-square test of independence gives the headline p-value; when the table is 2×2 or expected counts are sparse, Fisher's exact test provides the reliable verdict. Cramér's V (0 to 1) grades the strength of the association, and standardized residuals identify exactly which combinations are over- or under-represented.
Use it whenever both variables are categories — plan vs churn, channel vs conversion, shift vs defect — and you want to know if they're related and where.
Not for numeric-vs-numeric relationships (use the correlation tool) or numeric-vs-group comparisons (use a group-comparison test). It also won't rank multiple drivers at once — it tests one pair of columns.
Built for: Analysts and operators testing whether one categorical outcome depends on another categorical attribute
Typical data source: Any spreadsheet or CSV with two category-style columns (labels, segments, yes/no outcomes)
Any table with two categorical columns. For example, subscription accounts:
Minimum 20 rows · Best with 100-100,000 rows with 2-8 categories per column
Standard-library analysis: are two categorical columns related? Builds the full contingency table, runs the chi-square test of independence (with Fisher's exact test whenever counts are sparse or the table is 2×2), and sizes the relationship with Cramér's V — plus a heatmap of every combination, composition bars, and the specific cells that deviate most from independence. Works on any dataset: map two categorical columns.
Every combination of the two columns counted in one grid — hot cells show where your data concentrates.
Each group's composition side by side — identical profiles mean independence; shifting profiles are the association made visible.
Chi-square, Fisher's exact, and Cramér's V in one table, each with a plain-language interpretation.
The exact combinations that occur more or less often than independence predicts, ranked by standardized residual.
Plain-English interpretation — what the numbers mean, what's significant, and what to do next.
Does outcome depend on segment?
Map a segment column (plan, region, channel) against an outcome column (churned, converted, passed). The test tells you if they're related, Cramér's V tells you how much, and the deviation table names the exact combinations that are over- or under-represented.
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.
Run any analysis on your own data — validated R analyses, interactive reports, AI insights, and PDF export.
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