DA0-002 Data Analysis Practice Question

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

• ✗

  ANOVA

  Why it's wrong here

  ANOVA compares means across three or more groups on a continuous dependent variable, so it tests group differences rather than association between two categorical variables. It is tempting because it handles categorical factors, but it requires a continuous outcome, whereas chi-square tests independence of two categorical variables.

• ✓

  Chi-square test

  Why this is correct

  The chi-square test of independence compares observed versus expected frequencies across a contingency table, determining whether two categorical variables are associated. Gender and product preference are both nominal categories, so this test satisfies the requirement for assessing significant association between them.

• ✗

  Pearson correlation

  Why it's wrong here

  Pearson correlation measures the linear relationship between two continuous variables, so it cannot assess association between categorical variables like gender and product preference. It is tempting because both test relationships, but Pearson requires interval or ratio data, whereas chi-square operates on observed category frequencies.

• ✗

  t-test

  Why it's wrong here

  A t-test compares means between two groups on a continuous outcome, so it cannot test association between two categorical variables such as gender and product preference. It is tempting because gender is categorical, but the outcome must be continuous; chi-square instead compares observed versus expected category frequencies.