Which hypothesis is tested using expected frequency in Chi Square analysis?

In Chi Square analysis, the null hypothesis is tested using expected frequency. The null hypothesis typically states that there is no significant association or difference between the variables being analyzed. It provides a baseline against which the observed data can be compared.

To test this hypothesis, we calculate the expected frequencies based on the assumption that the null hypothesis is true. These expected frequencies represent how many observations we would expect in each category if there were no relationship between the variables. By comparing these expected frequencies with the observed frequencies from the sample data, we can determine whether the observed differences are statistically significant or could have occurred due to random chance.

If the Chi Square statistic indicates that the observed frequencies significantly differ from the expected frequencies, we may reject the null hypothesis. Conversely, if there is no significant difference, we fail to reject the null hypothesis, suggesting that the variables do not have a significant relationship. Thus, the null hypothesis plays a central role in Chi Square analysis, and expected frequencies are essential for evaluating it.