A/B testing Calculator: Chi-Square Test

The Chi-Square test determines if there is a significant relationship between categorical variables. Use it to test for independence between variables or to assess the goodness-of-fit between expected and observed frequencies.

The calculator uses data on the number of successes and the total number of users in each sample. Based on these values, it automatically generates a 2x2 table (variant A / variant B × success / failure) and calculates the χ² statistic.

A p-value less than 0.05 (typically) indicates a significant relationship between variables. The tool provides the chi-square statistic, degrees of freedom, and p-value for interpretation.

Chi-Square tests require: independent observations, categorical data, sufficient sample size, and random sampling from a general population.

This test is not recommended for very small data sets. In such cases, it is better to use Fisher's exact test.

Yes. The calculator is suitable for analyzing conversions, CTR, registrations, purchases, and other binary metrics that compare two user groups.

A Goodness-of-Fit test is used to check if observed frequencies of a single categorical variable match an expected distribution. An Independence test is used to determine if there is a relationship between two categorical variables.

The larger the sample size, the more reliable the results. If the sample size is too small, the test may fail to detect any real differences. For reliable conclusions, it's best to have at least several dozen conversions in each group.