Identify the non-parametric tests used in statistical analysis.

Correct option is A

Non-parametric tests are statistical tests that do not assume a specific probability distribution for the data. They are used when data does not meet the assumptions of normality, homogeneity of variance, or when dealing with ordinal and categorical data.

1. Chi-square test –  Correct (Non-parametric test)
   • The Chi-square test is a non-parametric test used to analyze categorical (nominal) data.
   • It evaluates whether observed frequencies differ from expected frequencies in contingency tables.
   • Used in goodness-of-fit tests and tests for independence in categorical data analysis.

Information Booster 

1. z-test –  Incorrect (Parametric test)

   • The z-test is a parametric test used for comparing sample means when the population variance is known and the sample size is large (n > 30).

2. Pearson correlation –  Incorrect (Parametric test)

   • The Pearson correlation is a parametric test that measures the linear relationship between two continuous variables assuming normal distribution.
   • If data does not meet normality assumptions, a Spearman rank correlation (non-parametric) is used instead.

3. t-test –  Incorrect (Parametric test)

   • The t-test is a parametric test used to compare the means of two groups when the population variance is unknown and the sample size is small (n < 30).
   • Independent t-tests and paired t-tests assume normal distribution.