All are true about Chi-Square test except
**Core Concept**
The Chi-Square test is a statistical method used to determine the significance of association between two categorical variables. It compares the observed frequencies in a sample with the expected frequencies, calculated based on a null hypothesis of no association.
**Why the Correct Answer is Right**
The Chi-Square test is used to assess the association between two categorical variables, such as the relationship between a disease and a risk factor. The test calculates the probability of observing the observed frequencies, assuming that there is no association between the variables. The Chi-Square statistic is calculated as the sum of the squared differences between the observed and expected frequencies, divided by the expected frequency. This statistic is then compared to a critical value from a Chi-Square distribution to determine the significance of the association.
**Why Each Wrong Option is Incorrect**
**Option A:** This option is incorrect because the Chi-Square test is used for categorical data, not continuous data.
**Option B:** This option is incorrect because the Chi-Square test assumes that the expected frequencies are at least 5, not that the observed frequencies are at least 5.
**Option C:** This option is incorrect because the Chi-Square test is used to determine the significance of association between two categorical variables, not to determine the mean or median of a continuous variable.
**Clinical Pearl / High-Yield Fact**
When using the Chi-Square test, it's essential to check for assumptions, such as the expected frequencies being at least 5, and to interpret the results in the context of the research question.
**Correct Answer: A.**