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10 PYQ related to statistical tests

Previous year questions (PYQ) and description related to various statistical tests from UGC NET and other competitive exams:

1. T-Test (UGC NET June 2023)
Question: Which of the following tests is appropriate to compare the means of two independent groups?
A) Z-Test
B) Chi-Square Test
C) Independent T-Test
D) ANOVA
Answer: C) Independent T-Test
Explanation: The Independent T-Test is used to compare the means of two independent groups to determine whether there is a significant difference between them.

2. Chi-Square Test (UGC NET December 2022)
Question: The Chi-Square Test for Independence is used to test the relationship between:
A) Two categorical variables
B) Two continuous variables
C) One categorical and one continuous variable
D) Three continuous variables
Answer: A) Two categorical variables
Explanation: The Chi-Square Test for Independence checks whether there is a relationship between two categorical variables (e.g., gender and voting preference).

3. ANOVA (UGC NET June 2022)
Question: Which statistical test is used to compare the means of three or more independent groups?
A) T-Test
B) Z-Test
C) ANOVA
D) Chi-Square Test
Answer: C) ANOVA
Explanation: ANOVA (Analysis of Variance) is used to test for significant differences between the means of three or more independent groups.

4. Mann-Whitney U Test (UGC NET December 2021)
Question: Which test should be used when comparing two independent groups with data that does not follow a normal distribution?
A) T-Test
B) Mann-Whitney U Test
C) ANOVA
D) Chi-Square Test
Answer: B) Mann-Whitney U Test
Explanation: The Mann-Whitney U Test is a non-parametric alternative to the T-Test, used when the data is not normally distributed.

5. Wilcoxon Signed-Rank Test (UGC NET June 2021)
Question: The Wilcoxon Signed-Rank Test is used to compare:
A) Two independent samples
B) Paired samples
C) Three or more groups
D) Population mean with sample mean
Answer: B) Paired samples
Explanation: The Wilcoxon Signed-Rank Test is a non-parametric test that compares paired or related samples.

6. Kruskal-Wallis Test (UGC NET December 2020)
Question: Which test is an alternative to One-Way ANOVA when the data is not normally distributed?
A) T-Test
B) Kruskal-Wallis Test
C) Z-Test
D) Chi-Square Test
Answer: B) Kruskal-Wallis Test
Explanation: The Kruskal-Wallis Test is a non-parametric alternative to One-Way ANOVA and is used to compare the medians of three or more independent groups.

7. Pearson's Correlation (UGC NET June 2020)
Question: Which of the following correlation coefficients indicates the strongest positive linear relationship?
A) +0.85
B) -0.75
C) +0.50
D) -0.90
Answer: A) +0.85
Explanation: A correlation coefficient close to +1 indicates a strong positive linear relationship between variables.

8. Spearman's Rank Correlation (UGC NET December 2019)
Question: Which statistical test is used to measure the strength of the association between two ranked variables?
A) Pearson's Correlation
B) Spearman's Rank Correlation
C) ANOVA
D) Z-Test
Answer: B) Spearman's Rank Correlation
Explanation: Spearman's Rank Correlation is a non-parametric test that measures the relationship between two ranked variables.

9. Regression Analysis (UGC NET June 2019)
Question: The primary purpose of regression analysis is to:
A) Test the difference between two means
B) Predict the value of a dependent variable
C) Determine the association between categorical variables
D) Test for normal distribution
Answer: B) Predict the value of a dependent variable
Explanation: Regression analysis predicts the dependent variable based on the relationship with one or more independent variables.

10. Fisher's Exact Test (UGC NET December 2018)
Question: In which situation is the Fisher’s Exact Test preferred over the Chi-Square Test?
A) When the sample size is large
B) When the data is normally distributed
C) When the sample size is small
D) When comparing means
Answer: C) When the sample size is small
Explanation: The Fisher’s Exact Test is used instead of the Chi-Square Test when sample sizes are too small to meet the assumptions of the Chi-Square Test.

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