**Question:** Not true about chi-square test is ?

A. Chi-square test is applicable only for continuous variables
B. The test is sensitive to large sample sizes
C. The test does not account for the correlation between variables
D. The test is suitable for small sample sizes

**Correct Answer:**

**Core Concept:** The chi-square test is a statistical test used to determine if there is a significant relationship between two categorical variables. It is commonly used in medical research to assess associations between variables like disease prevalence and risk factors.

**Why the Correct Answer is Right:**

The chi-square test is considered appropriate when the data are categorical in nature and not continuous. It examines the independence between two categorical variables by comparing the observed and expected frequencies. The test assumes that the expected frequencies are greater than or equal to 5, which is not true for small sample sizes. This means that option D is incorrect because the test requires a minimum of five expected frequencies in each cell of the contingency table.

**Why Each Wrong Option is Incorrect:**

Option A is incorrect because the chi-square test is applicable for categorical variables, not continuous ones, which are measured on a numerical scale.

Option B is incorrect because the chi-square test is sensitive to large sample sizes, not the other way around. In fact, larger sample sizes increase the test's power to detect significant associations.

Option C is incorrect because the chi-square test does not consider the correlation between variables. It assumes independence between variables, so a significant result does not imply a causal relationship between them.

**Clinical Pearl:** When analyzing categorical data, ensure that the chi-square test is used correctly based on the nature of the variables and their expected frequencies. Understanding these nuances helps you apply statistical tests appropriately, ensuring your conclusions are valid and reliable.