Match the List – I (Statistical Test) with List – II (Descriptions):
| List I (Statistical Test) | List II (Descriptions) |
| (a) The ‘t’ test | (i) Similarities of difference between two or more frequency distribution |
| (b) Chi square | (ii) Comparison between means |
| (c) ANOVA | (iii) Uses principal component analysis |
| (d) Factor analysis | (iv) Uses F statistic |
| Codes: | (a) | (b) | (c) | (d) |
| (A) | (i) | (iii) | (ii) | (iv) |
| (B) | (iii) | (ii) | (iv) | (i) |
| (C) | (ii) | (i) | (iv) | (iii) |
| (D) | (iv) | (iii) | (i) | (ii) |
Correct Ans: (A)
Explanation:
Researchers use the t-test to compare the means of two groups. They apply it to check if the average values of these groups differ significantly. This test plays a crucial role in experiments involving two sample groups.
Analysts use the Chi-square test to examine the similarities or differences between two or more frequency distributions. They primarily rely on it to test independence or goodness of fit in categorical data analysis.
With ANOVA (Analysis of Variance), statisticians use the F-statistic to compare the means of three or more groups. This test helps them determine if group means vary significantly while accounting for within-group variability. Researchers often use ANOVA to extend the t-test when comparing multiple groups.
Data scientists apply Factor Analysis to reduce dimensionality and uncover underlying structures in a dataset. They often rely on principal component analysis (PCA) to group variables based on correlations. This method helps them simplify complex datasets and identify hidden patterns.
In summary, statisticians use these tests for specific analytical purposes, enabling them to understand relationships, differences, and patterns in datasets effectively.
