Yates' Correction

Definition

Yates' correction is a statistical adjustment used in the analysis of 2x2 contingency tables, particularly when the expected cell frequencies are small. It is designed to provide a more accurate p-value for the chi-square test of independence by compensating for the overestimation of the test statistic when the sample size is small.

5 Must Know Facts For Your Next Test

1. Yates' correction is specifically designed for 2x2 contingency tables, where the sample size is small, and the expected cell frequencies are less than 5.
2. The correction adjusts the chi-square test statistic by subtracting 0.5 from the absolute value of the difference between the observed and expected frequencies in each cell.
3. Yates' correction reduces the chi-square test statistic, leading to a more conservative p-value and a lower likelihood of rejecting the null hypothesis of independence.
4. The use of Yates' correction is recommended when the assumptions for the standard chi-square test of independence are not met, such as when the sample size is small or the expected cell frequencies are low.
5. Yates' correction is particularly important in medical and epidemiological studies, where small sample sizes and rare events are common.

Review Questions

• Explain the purpose of Yates' correction in the context of contingency tables.
  • The purpose of Yates' correction is to adjust the chi-square test statistic when analyzing 2x2 contingency tables with small sample sizes or low expected cell frequencies. This correction helps to compensate for the overestimation of the test statistic, which can occur when the assumptions of the standard chi-square test are violated. By making this adjustment, Yates' correction provides a more accurate p-value and helps researchers avoid making incorrect conclusions about the independence of the variables in the contingency table.
• Describe how Yates' correction is applied to the chi-square test statistic.
  • Yates' correction is applied by subtracting 0.5 from the absolute value of the difference between the observed and expected frequencies in each cell of the 2x2 contingency table. This adjustment reduces the chi-square test statistic, resulting in a more conservative p-value. The rationale behind this correction is to account for the discontinuity of the chi-square distribution, which becomes more pronounced when the expected cell frequencies are small. By applying Yates' correction, researchers can obtain a more accurate assessment of the statistical significance of the relationship between the variables in the contingency table.
• Analyze the importance of Yates' correction in the context of medical and epidemiological studies.
  • In medical and epidemiological studies, small sample sizes and rare events are common, which can violate the assumptions of the standard chi-square test of independence. In these situations, Yates' correction becomes particularly important. By applying Yates' correction, researchers can obtain more reliable p-values when analyzing the relationship between categorical variables, such as the presence or absence of a disease and a potential risk factor. This is crucial in these fields, where accurate statistical inference can inform important clinical decisions and public health interventions. The use of Yates' correction helps researchers avoid making Type I or Type II errors, which could have significant implications for patient care and population health.

Related terms

Contingency Table:

A contingency table is a type of table used to display the frequency distribution of two or more categorical variables, allowing for the analysis of the relationship between them.

Chi-Square Test of Independence:

The chi-square test of independence is a statistical test used to determine whether there is a significant relationship between two categorical variables in a contingency table.

Expected Cell Frequencies: In a contingency table, the expected cell frequencies represent the values that would be expected in each cell if the null hypothesis of independence between the variables is true.