The Chi-square test is a statistical method used to determine whether there is a significant relation between categorical variables.
Thank you for reading this post, don't forget to subscribe!- The goal of chi-square test is to identify whether a difference between actual and predicted data is due to chance or to a link between the variable under consideration.
- It is widely used in research to test hypotheses about the relationship between two or more categories of data.
- Unlike tests for numerical data, the chi-square test deals with frequencies or counts rather than means or standard deviations.
The assumptions of a Chi-Square Test:
- Both variables are categorical
- All observations are independent
- Cells in the contingency table are mutually exclusive
- Expected value of cell should be 5 or greater than in at least 80% of cells.
Goodness of Fit Chi-Square
The non-parametric tests are used when we do not know the distribution.
A test that compares the observations with the assumed or expected frequencies to how well it fits the observed frequencies is called a goodness of fit test.
- In Chi-Square Goodness of Fit Test, the term goodness of fit is used to compare the observed sample distribution with the expected sample distribution.
Assumptions of Chi-Square Goodness of Fit Test
- At least one variable should be categorical
- Observations must be independent
- The group of categorical variables should be mutually exclusive
- The expected frequency of each group of the categorical variable is at least 5
Process to Chi-Square Goodness of Fit Test
- Calculate the expected frequencies
- Calculate chi-square
- Find the critical chi-square value
- Compare the chi-square value to the critical value
- Decide whether to reject null hypothesis