- #1
Agent Smith
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- TL;DR Summary
- Chi-square tests for homogeneity and association, how to tell the difference?
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From the above example questions, we have ##2## different kinds of Chi-square tests.
1. One for homogeneity
2. One for association (independence/dependence).
The answer guide says that if we take ##1## sample, we're testing for association. The geneticist took ##1## sample of ##500## peeps. If we take ##2## samples then we're testing for homogeneity. The market researcher takes ##2## samples, 180 sedans and 180 trucks.
I can make some sense of a Chi-square test for homogeneity. We take ##2## samples i.e. there are ##2## different categories (in the questions above, cars and sedans) and see if the distribution of colors for these ##2## categories differ in a statistically significant way or not.
But I have trouble with Chi-square tests for association. In the above example, aren't we checking for the "distribution" of handedness (left/right/both) for the ##2## categories, men and women? They look same to me. Suppose I had taken, could I?, ##2## identical samples and got the exact same data. Does the Chi-square computation (which doesn't seem to distinguish the ##2## cases) now become one for homogeneity?
Gracias.