A test for homogeneity is a statistical test used to determine if different samples come from populations with the same distribution. It uses the chi-square statistic to compare observed frequencies across multiple categories.
5 Must Know Facts For Your Next Test
The null hypothesis in a test for homogeneity states that all populations have the same distribution.
It is commonly used when analyzing categorical data from two or more independent groups.
The chi-square statistic is calculated by comparing observed and expected frequencies in each category.
Degrees of freedom for the test are calculated as $(r-1) \times (c-1)$, where $r$ is the number of rows and $c$ is the number of columns in the contingency table.
Rejecting the null hypothesis indicates that at least one population has a different distribution.
Review Questions
What does the null hypothesis state in a test for homogeneity?
How are degrees of freedom calculated in this test?
What type of data is analyzed using a test for homogeneity?
Related terms
Chi-Square Statistic: A measure used in statistics to assess how observed counts differ from expected counts under a specific hypothesis.
Contingency Table: A table used to display the frequency distribution of variables and analyze relationships between them.
Null Hypothesis: A statement assuming no effect or no difference, which researchers aim to test against an alternative hypothesis.