**Core Concept**
The Chi-square test is a statistical method used to determine whether there is a significant association between two categorical variables. It is commonly employed in epidemiological studies, clinical trials, and observational research to assess the relationship between variables such as disease prevalence and risk factors.

**Why the Correct Answer is Right**
The Chi-square test works by comparing the observed frequencies of each category with the expected frequencies under the assumption of no association. The test statistic is calculated as the sum of the squared differences between observed and expected frequencies, divided by the expected frequencies. This results in a chi-square value, which is then compared to a critical value from a chi-square distribution table or calculated using software. If the calculated chi-square value exceeds the critical value, the null hypothesis of no association is rejected, indicating a statistically significant association between the variables.

**Why Each Wrong Option is Incorrect**

* **Option A:** This option is incorrect because the Chi-square test is not used for comparing means of continuous variables. Tests like the t-test or ANOVA are more appropriate for this purpose.
* **Option B:** This option is incorrect because the Chi-square test is not used for regression analysis, which involves modeling the relationship between a dependent variable and one or more independent variables.
* **Option C:** This option is incorrect because the Chi-square test is not used for time-series analysis, which involves analyzing data collected over time.

**Clinical Pearl / High-Yield Fact**
The Chi-square test is sensitive to sample size, and large samples can lead to statistically significant results even if the association between variables is clinically insignificant. Therefore, it is essential to interpret results in the context of the study design, sample size, and clinical relevance.

**Correct Answer:** C. Testing association between categorical variables.