**Core Concept**
The chi-square test is a statistical method used to compare observed frequencies in categorical data with expected frequencies, thereby determining whether there is a significant difference between two or more proportions. This test is commonly employed in epidemiological studies and research to evaluate associations between variables.

**Why the Correct Answer is Right**
The chi-square test specifically measures the significance of the difference between two or more proportions by comparing the observed frequencies with the expected frequencies under the null hypothesis. The null hypothesis states that there is no significant difference between the proportions. The test calculates the chi-square statistic, which is then compared to a critical value or p-value to determine the significance of the difference. This is a critical tool in hypothesis testing, allowing researchers to assess whether observed differences are due to chance or if they are statistically significant. The chi-square test is particularly useful for analyzing categorical data, such as disease prevalence, risk factors, or treatment outcomes.

**Why Each Wrong Option is Incorrect**
**Option A:** The null hypothesis in the chi-square test is not necessarily equal; rather, it states that there is no significant difference between the proportions.
**Option B:** The chi-square test does indeed test the significance of the difference between two or more proportions, making this statement incorrect.
**Option D:** The chi-square test is not used to test correlation and regression, which are statistical methods used to analyze relationships between continuous variables.

**Clinical Pearl / High-Yield Fact**
A key aspect of the chi-square test is that it assumes independence between the observations, meaning that each observation is not influenced by the others. This is crucial to ensure the validity of the test results.

**Correct Answer:**
✓ Correct Answer: C. Measures the significance of difference between two proportion