A public health researcher is comparing the effectiveness of two malaria prevention strategies in a rural Indian district. Strategy A (bed nets) was implemented in 500 households, with 45 cases of malaria. Strategy B (indoor spraying) was implemented in 480 households, with 28 cases of malaria. Which statistical test is most appropriate to determine if the difference in malaria incidence between the two strategies is statistically significant?

Explanation

## Test Selection for Categorical Data Comparison ### Data Type Identification **Key Point:** The outcome variable (malaria: yes/no) is categorical/dichotomous, and we are comparing two independent groups (Strategy A vs Strategy B). ### Why Chi-Square Test? **High-Yield:** Chi-square test of independence is used when: - Both variables are categorical (exposure/intervention and outcome) - Comparing two or more independent groups - Sample sizes are adequate (expected frequency ≥ 5 in each cell) ### Contingency Table Structure | Strategy | Malaria Cases | No Malaria | Total | |----------|---------------|-----------|-------| | A (Bed nets) | 45 | 455 | 500 | | B (Spraying) | 28 | 452 | 480 | | **Total** | **73** | **907** | **980** | **Clinical Pearl:** The chi-square statistic tests whether the observed frequencies differ significantly from expected frequencies under the null hypothesis of independence (no difference between strategies). ### Formula $$\chi^2 = \sum \frac{(O - E)^2}{E}$$ where O = observed frequency, E = expected frequency. **Mnemonic:** **CATEGORICAL → CHI-SQUARE** — When you have categories (yes/no, present/absent), use chi-square; when you have continuous measurements, use t-test.