Which of the following is a non-parametric test?

Non-parametric tests are statistical tools used when data do not meet the assumptions of a normal distribution. These tests do not rely on parameters such as the mean or standard deviation of a population. They are often used for ordinal or nominal data or when the sample size is too small to assume normality. These methods provide a robust way to analyze data that is skewed or contains significant outliers. Rationale: A. The t-test is a classic parametric test used to compare the means of two independent groups. It assumes that the data follow a normal distribution and that the variances between the groups are equal. Because it relies on the calculation of a specific population parameter (the mean), it does not qualify as non-parametric. It is highly sensitive to non-normal data. B. Chi-square test is the correct answer as it is a widely used non-parametric test for categorical data. it evaluates the difference between observed and expected frequencies rather than comparing means. It makes no assumptions about the underlying distribution of the population. This makes it ideal for analyzing proportions and associations between nominal variables in clinical research. C. ANOVA (Analysis of Variance) is a parametric method used to compare the means of three or more groups. Like the t-test, it assumes normality and homoscedasticity (equal variance) across all groups being tested. It uses the F-statistic to determine if any group mean is significantly different. It is not suitable for data that violates parametric assumptions. D. The paired t-test is a parametric procedure designed to compare means from the same group at two different times. It relies on the assumption that the differences between the pairs are normally distributed. Since it is a variation of the standard t-test using mean values, it is classified as parametric. It cannot be used for non-normally distributed dependent samples.