How to perform a Yates' chi-square test?

To perform a Yates' chi-square test, first create a contingency table with two categorical variables.

Next, calculate the expected values for each cell in the table using the formula:
Expected value = (row total x column total) / grand total

Then, calculate the chi-square statistic using the formula:
Chi-square = Σ[(observed value - expected value)^2 / expected value]

Finally, determine the degrees of freedom (df) using the formula:
df = (number of rows - 1) x (number of columns - 1)

Compare the calculated chi-square value to the critical value from the chi-square distribution table at the given level of significance and degrees of freedom. If the calculated chi-square value is greater than the critical value, reject the null hypothesis and conclude that there is a significant association between the two variables.

It is important to note that Yates' correction is used when the sample size is small (less than 20) and the expected values are less than 5 in at least 20% of the cells. The correction involves adjusting the chi-square statistic by subtracting 0.5 from the absolute value of the difference between each observed and expected value before squaring and dividing by the expected value.

Overall, Yates' chi-square test is a useful tool for determining if there is a significant association between two categorical variables.