It aims at correcting the error introduced by assuming that the discrete probabilities of frequencies in the table can be approximated by a continuous distribution (chi-squared).
To reduce the error in approximation, Frank Yates, an English statistician, suggested a correction for continuity that adjusts the formula for Pearson's chi-squared test by subtracting 0.5 from the difference between each observed value and its expected value in a 2 × 2 contingency table.
The effect of Yates's correction is to prevent overestimation of statistical significance for small data.
The following is Yates's corrected version of Pearson's chi-squared statistics: where: As a short-cut, for a 2 × 2 table with the following entries: In some cases, this is better.
[citation needed] However, in situations with large sample sizes, using the correction will have little effect on the value of the test statistic, and hence the p-value.