I used this answer as my topic Difference between a chi-square test for independence and a chi-square goodness of fit test

• The assumption under which the expected numbers are derived is the main distinction between the goodness of fit test and the test of independence. The expected counts are determined in the case of the goodness of fit under the assumption that the sample originates from the hypothesized distribution. The predicted counts are determined in the case of the test of independence under the assumption that the two variables are independent.

How would you know which test to use?

• In the test of independence, two variables are observed for each observational unit.
• In the goodness-of-fit test, there is only one observed variable.

Explain the different variables you would use for each test and why.

• The test of independence presumes that you have 2 random variables, and you want to test their independence given the sample at hand.
• The goodness of fit test, on the other hand, works on 1 random variable at a time
• My professor asked me this question?
• If your population of interest is college students, would you expect to see a significant linear relationship between age and height?

Can you help me answer?