Explain the concept of homogeneity in the context of chi-squared tests.
Tests if different populations are similar in their distribution of a categorical variable.
Explain the concept of independence in the context of chi-squared tests.
Tests if two variables are not associated with each other.
Explain the purpose of calculating expected counts.
To determine what the cell values would be if there is no association between the variables.
What are the differences between the Chi-Squared Test for Homogeneity and the Chi-Squared Test for Independence?
Homogeneity: Compares across populations, looking for similar distributions. | Independence: Looks within a single population, testing for associations between variables.
Compare and contrast the null hypothesis for the Chi-Squared Test for Homogeneity and Independence.
Homogeneity: The distribution of the categorical variable is the same across all populations. | Independence: There is no association between the two categorical variables.
What is a two-way table?
A grid showing the relationship between two categorical variables.
Define Chi-Squared Test for Homogeneity.
Compares the distribution of a categorical variable across two or more independent groups/populations.
Define Chi-Squared Test for Independence.
Examines the relationship between two categorical variables within a single population.
What are expected counts?
The counts we expect to see if there's no association (independence) or no difference (homogeneity).
Define Null Hypothesis.
States that there is no association between the two categorical variables.
