DSRT 734: Choosing a Test for Gender and Disease

DSRT 734 Question

A researcher wants to test if gender is related to a certain disease.
The collect the following data. What is the correct test for them to run?


                    Disease Yes        Disease No

Male                12                 21

Female              7                  23


Option A

Spearman's rho correlation


Option B

Pearson's Correlation


Option C

Pearson's Chi-Square


Option D

Fisher's Exact Test

What this question assesses

This item assesses how two binary categorical variables and a 2-by-2 table determine the relevant family of analyses, including the role of expected cell counts in choosing an approximation or exact procedure.

How to approach this type of question

Start with the observed table rather than the names of familiar tests. Both gender and disease status are represented as categories, and every person should contribute to one cell. Compute row totals, column totals, and the grand total. From those margins, calculate expected counts under independence and examine whether the approximation conditions taught in the course are met. That expected-count check distinguishes between categorical procedures that may otherwise look interchangeable in the answer list. Correlation coefficients for quantitative or ranked pairs and mean-comparison procedures answer different questions. If the chosen categorical method produces evidence of a relationship, describe association rather than causation and consider reporting proportions or an effect-size measure for context. Apply the checks to the table and options. This page intentionally does not name the correct choice.

Validate the table by confirming that row and column totals equal the grand total. Calculate expected counts with full precision before applying the course criterion, and keep the exact-test p-value separate from the asymptotic result if both are displayed. A useful final explanation would pair the statistical decision with disease proportions by gender and an appropriate effect measure. It should not label gender as a causal mechanism based on this table alone.

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Related DSRT 734 resources

• University of the Cumberlands course resources
• DSRT 734 Inferential Statistics in Decision-Making
• DSRT 734 Questions: Inferential Statistics in Decision-Making
• How to Choose an Inferential Statistical Test