Chi-Square Test Calculator
Test whether two categorical variables are related (test of independence) or whether observed frequencies match expected proportions (goodness of fit). Paste your contingency table — any size from 2×2 upward — and get the χ² statistic, degrees of freedom, exact p-value, effect sizes (Cramér's V, and φ for 2×2 tables) plus the full table of expected counts. For 2×2 tables the Yates continuity correction is applied by default, matching R's chisq.test(); you can switch it off with one click. When any expected count falls below 5, the calculator warns you automatically — the classic condition for chi-square validity. All results verified against R to at least six significant digits.
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Let AI interpret your results: a downloadable Word document in APA 7 / business report format.
Frequently asked questions
How do I report chi-square results in APA 7 format?
Report χ² with degrees of freedom and sample size, the p-value and an effect size, e.g.: "Treatment and outcome were significantly associated, χ²(1, N = 80) = 4.14, p = .042, φ = .25." The AI Report button produces the complete APA 7 write-up from your table.
When should I use the Yates continuity correction?
Only for 2×2 tables. It makes the test slightly conservative, compensating for the discreteness of counts; R applies it by default. For larger tables it is not used. If your expected counts are very small, consider Fisher's exact test instead.
What is Cramér's V and how large should it be?
Cramér's V scales the chi-square statistic to a 0–1 range so tables of different sizes can be compared. Rough benchmarks for a 2×2 table: .10 small, .30 medium, .50 large association.
What does the 'expected count below 5' warning mean?
The chi-square p-value is an approximation that becomes unreliable when expected cell counts are small. The common rule: all expected counts ≥ 5 (or at least 80% of them). With smaller counts, combine categories or use Fisher's exact test.