Yates' correction of contingency is a statistical method used to adjust for potential bias in the calculation of contingency tables or chi-square tests.
Yates' correction is necessary because traditional contingency tables or chi-square tests can overestimate the significance of relationships between variables due to small sample sizes or sparse data.
Yates' correction subtracts a constant value, typically 0.5, from the absolute difference between the observed and expected values in a contingency table. This helps to reduce the bias in the calculation and provide a more accurate measure of the relationship between variables.
Yates' correction should be used when conducting a chi-square test or analyzing a contingency table with small sample sizes or sparse data. It is recommended for use when the expected values in the table are less than 5.
Yes, Yates' correction is not always appropriate for all types of data. It should not be used when the expected values in a contingency table are very small or when there are more than two categories in each variable. In these cases, alternative methods such as Fisher's exact test may be more appropriate.