ds@mcpanalytics.aiA data scientist you can email your data and question to, and get a reliable response. Not AI slop.A data scientist you can email. Not AI slop.
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.
Free analyses run on up to 10,000 rows. Larger files are randomly sampled to that size, so sign up to analyze your full dataset.
Cross-tabulating and testing for association...
Sent to . Inside: contingency heatmap, chi-square + Fisher exact results, Cramér's V, largest deviations, R code, and AI insights.
Analyze another fileThe practical guide behind this tool: when it applies, how to read the output, and the traps that make it say the wrong thing.
The 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 of 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: R analyses, interactive reports, AI insights, and PDF export.
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CympleData Scientist Send me your data and question, I’ll send you the analytics. ds@mcpanalytics.ai
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