A public health researcher in Delhi is investigating whether the prevalence of hypertension differs significantly between two occupational groups: factory workers (n=150) and office workers (n=150). The data are categorical (hypertensive vs. non-hypertensive). Which statistical test is most appropriate to compare the prevalence of hypertension between these two independent groups?

## Rationale for Test Selection **Key Point:** When comparing categorical outcomes (presence/absence of disease) between two or more independent groups, the chi-square test of independence is the test of choice. ### Data Type Analysis - **Dependent variable:** Hypertension status (categorical: yes/no) - **Independent variable:** Occupational group (categorical: factory vs. office) - **Sample structure:** Two independent, unrelated groups ### Why Chi-Square Test? The chi-square test evaluates whether there is a statistically significant association between two categorical variables. It compares observed frequencies with expected frequencies under the null hypothesis of independence. **Formula:** $$\chi^2 = \sum \frac{(O - E)^2}{E}$$ where O = observed frequency, E = expected frequency. ### Contingency Table Setup | Occupational Group | Hypertensive | Non-hypertensive | Total | |---|---|---|---| | Factory workers | a | b | 150 | | Office workers | c | d | 150 | | **Total** | **a+c** | **b+d** | **300** | **High-Yield:** The chi-square test assumes: - Expected frequency in each cell ≥ 5 (if violated, use Fisher's exact test for 2×2 tables) - Independent observations - Categorical data ### Degrees of Freedom For a 2×2 contingency table: df = (rows − 1) × (columns − 1) = 1 **Clinical Pearl:** This is the standard epidemiological approach to compare disease prevalence across exposure groups — the foundation of case-control and cross-sectional study analysis.