Any table size
2×2, 3×4, or larger. Degrees of freedom are computed from the shape you paste.
Tools / Chi Square Calculator
Paste a contingency table, one row per line, and get the chi-square statistic, degrees of freedom, p-value, and effect size.
χ² = Σ (O − E)² ÷ E · df = (rows − 1)(columns − 1)
Paste a table of counts to see the result
One row per line, values separated by commas. Rows are one variable and columns the other.
Whole numbers of people or events, never percentages or averages. That is the most common mistake with this test.
Below 0.05 means the two variables are unlikely to be independent. Cramer's V tells you how strong the association is.
When both variables are categories rather than numbers, this is the test that tells you whether they are related.
2×2, 3×4, or larger. Degrees of freedom are computed from the shape you paste.
Cramer's V sits next to the p-value, because a big table with a big sample can be significant and still trivial.
If any expected cell drops below 5 the result flags it, since the chi-square approximation gets shaky there.
Does answer choice depend on region, plan, or age group?
Compare conversion counts across three or more variants in one test.
Any question where both the row and the column are labels rather than measurements.
χ² = Σ (O − E)² ÷ E where E = (row total × column total) ÷ grand total · df = (rows − 1)(columns − 1)
O is the observed count in a cell and E is the count you would expect if the two variables were independent. Cramer's V rescales χ² to a 0 to 1 effect size using the sample size and the smaller table dimension.
For each cell, compute the expected count as (row total × column total) ÷ grand total. Then sum (observed − expected)² ÷ expected across every cell.
Degrees of freedom are (rows − 1) × (columns − 1), and the p-value comes from the chi-square distribution with that df.
A grid of counts crossing two categorical variables. Rows might be “used the coupon” and “did not”, while columns are “new customer” and “returning”.
Each cell holds how many people fall into that combination. Enter one row per line here, comma separated.
That the row and column variables are unlikely to be independent. Knowing one tells you something about the other.
It does not tell you which cells drove the result. Compare observed against expected counts cell by cell to find that.
An effect size from 0 to 1 derived from chi-square. Roughly, 0.1 is a weak association, 0.3 moderate, and 0.5 or above strong.
It matters because with a large enough sample almost any table becomes statistically significant while the association stays practically meaningless.
The chi-square distribution is an approximation that degrades when expected cell counts are very small. The usual guidance is that most expected counts should be 5 or more.
With a small 2×2 table, Fisher's exact test is the better choice.
No. The test relies on actual counts to estimate variability. Entering percentages will produce a number, but it will not be a valid test. Convert back to raw counts first.
For exactly two groups and one yes/no outcome they agree, and the statistical significance calculator also gives you the lift.
Use chi-square when you have more than two groups or more than two outcome categories.
No. Parsing and testing happen in your browser.
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