Yates's correction for continuity

In statistics, Yates's correction for continuity (or Yates's chi-squared test) is a statistical test commonly used when analyzing count data organized in a contingency table, particularly when sample sizes are small. It is specifically designed for testing whether two categorical variables are related or independent of each other. The correction modifies the standard chi-squared test to account for the fact that a continuous distribution (chi-squared) is used to approximate discrete data. Almost exclusively applied to 2×2 contingency tables, it involves subtracting 0.5 from the absolute difference between observed and expected frequencies before squaring the result.

Unlike the standard Pearson chi-squared statistic, Yates's correction is approximately unbiased for small sample sizes. It is considered more conservative than the uncorrected chi-squared test, as it increases the p-value and thus reduces the likelihood of rejecting the null hypothesis when it is true. While widely taught in introductory statistics courses, modern computational methods like Fisher's exact test may be preferred for analyzing small samples in 2×2 tables, with Yates's correction serving as a middle ground between uncorrected chi-squared tests and Fisher's exact test.

The correction was first published by Frank Yates in 1934.^[1]