Solution: Parametric Test Matching

This question requires matching specific statistical tests (LIST-I) with their corresponding p-values (LIST-II). The focus is on parametric tests. The provided p-values are all relatively high, indicating non-rejection of the null hypothesis in each scenario at conventional significance levels.

Matching Parametric Tests with P-values

Based on the correct answer provided, the matching is as follows:

• A. One Mean is matched with I. p-value $0.474$. This typically corresponds to a one-sample t-test or z-test, where the p-value suggests no significant difference from the hypothesized population mean.
• B. Two Independent Means is matched with IV. p-value $0.564$. This relates to an independent samples t-test, indicating no statistically significant difference between the means of two unrelated groups.
• C. Two Dependent Means is matched with III. p-value $0.550$. This aligns with a paired samples t-test, suggesting no significant difference between the means of two related measurements (e.g., before and after).
• D. Correlation (Pearson's) is matched with II. p-value $0.703$. This signifies a test for the linear association between two variables. The high p-value indicates a lack of significant linear correlation.

Understanding P-values in Hypothesis Testing

In hypothesis testing, the p-value quantifies the probability of observing test results at least as extreme as the results actually observed, assuming the null hypothesis is true. A high p-value (typically > $0.05$) suggests that the observed data are not sufficiently unusual to reject the null hypothesis.

The matching reflects plausible scenarios where parametric tests were conducted, and the results did not meet the threshold for statistical significance.