Statistical Tests

If you export raw data from Elaiia for your own analysis, here are the most common tests you might use and when they apply.

t-Test

Compares the means of two groups to determine if they differ significantly. Use an independent-samples t-test for between-subject designs (Group A vs. Group B) and a paired t-test for within-subject designs (same twins, two conditions).

When to use: “Do Gen Z twins rate this concept higher than Millennials?” or “Did satisfaction increase after seeing the ad?”

ANOVA (Analysis of Variance)

Extends the t-test to three or more groups. Tests whether at least one group mean differs from the others. Follow up with post-hoc tests (e.g. Tukey) to find which specific groups differ.

When to use: “Do satisfaction ratings differ across three age segments?” or “Which of four concepts scores highest?”

Correlation

Measures the strength and direction of the relationship between two variables. A correlation of +1 means a perfect positive relationship, −1 a perfect inverse relationship, and 0 no linear relationship.

When to use: “Is there a relationship between brand awareness and purchase intent?” Remember: correlation does not imply causation.

Chi-Square Test

Tests whether the distribution of categorical responses differs from what you’d expect by chance, or whether two categorical variables are associated.

When to use: “Do men and women choose different packaging options?” or “Is brand preference independent of age group?”