Tests of Significance — t, chi-square MCQ — NEET PG Practice Question | NEETPGAI
Tests of Significance — t, chi-square
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A public health researcher in Delhi conducted a study comparing the effectiveness of two different health education interventions (video-based vs. pamphlet-based) on reducing tobacco use among 120 factory workers. The outcome was binary (quit smoking: yes/no). After data collection, the researcher needs to determine whether there is a statistically significant difference in quit rates between the two groups. What is the most appropriate next step in statistical analysis?
A. Perform a chi-square test of independence on the 2×2 contingency table
B. Perform an independent samples t-test on the quit rates
C. Calculate the correlation coefficient between intervention type and outcome
D. Perform a paired t-test since both groups received interventions
Explanation
Test Selection for Categorical Data
Key Point
When comparing categorical outcomes (binary: yes/no) across two independent groups, the chi-square test of independence is the gold standard.
Why Chi-Square is Correct
The data structure is:
Independent variable: Intervention type (categorical: video vs. pamphlet)
Dependent variable: Smoking cessation outcome (categorical: quit vs. not quit)
Sample size: n = 120 (adequate for chi-square)
This creates a 2×2 contingency table:
Table
Intervention
Quit
Not Quit
Total
Video
a
b
a+b
Pamphlet
c
d
c+d
Total
a+c
b+d
120
The chi-square test statistic is:
χ2=(a+b)(c+d)(a+c)(b+d)N(ad−bc)2
High-YieldNEET PG
Chi-square tests categorical association; t-tests compare means of continuous variables.
Assumptions Met
Both variables are categorical (nominal)
Groups are independent (different workers, different interventions)
Expected frequency in each cell ≥ 5 (likely, given n = 120)
Clinical Pearl
In public health intervention studies, binary outcomes (disease present/absent, behavior adopted/not adopted) are extremely common—chi-square is your go-to test.
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