Inferential Statistic Test of Independence Flashcards
(Greek symbol chi “χ”)
Chi-Square
Used to investigate whether distributions of categorical variables differ from one another.
Chi-square
Observations must be independent and same observation can only appear in on cell.
Chi-square
This test statistics assumes a non-directional hypothesis and tests the hypothesis that two variables are related only by chance.
Chi-Square
Non-parametric and used to analyze frequencies.
Chi-square
Also called Pearson’s chi-square test or the chi square test of association, is used to test the relationship between two categorical variables.
Chi-Square
2 Types of Chi-Square
- Chi-square test for independence
- Chi square Goodness of Fit Test
This test checks if there is a relationship or association between two categorical variables eg. there a relationship between a person’s age group and their drink preference? In this case, both variables (age and drink preference) are categorical.
[types of chi-square]
Chi-square test for independence
This test checks if our data fits an expected distribution. Eg. , if we expect that the number of people who prefer coffee, tea, and juice are equal in a population, we use this test to see if our sample data matches that expected distribution.
[types of chi-square]
Chi-square Goodness of Fit test
These are the actual counts or numbers we collect during our research. For example, suppose we conduct a survey to find out the favorite drinks of people across different age groups.
[frequencies]
Observed frequencies
These are the counts we would expect to see if there was no relationship between the variables. Essentially, we’re assuming that any differences we observe are purely due to chance, not because one variable influences the other.
Expected frequencies
