What is a Chi-Square Test?
The chi-square test is a statistical test that can be used when we want to determine whether some observed frequencies deviate from the frequencies we would expect under a certain hypothesis. With this test, we examine whether there is an association between two variables, and it shows the probability of association. The Chi-square test is used when we have two categorical variables with two or more groups.
When Should a Chi-Square be Used?
You can use a chi-square test of independence when you have two categorical variables. It allows you to test whether the two variables are related to each other. If two variables are independent (unrelated), the probability of belonging to a certain group of one variable isn’t affected by the other variable
An Example Of a Chi-Square Test
For example, a researcher wants to check if there is a relationship between gender and math exam results. In that case, we have two categorical variables: gender (male, female) and math exam result (passed the exam or not).
What are the use of null and alternative hypothesis for the Chi-Square Test?
Therefore, we test the following hypotheses:
Null hypothesis: There is no association between gender and math exam scores.
Alternative hypothesis: There is an association between gender and math exam scores.
R function to Compute Chi-Square Test
The code to run a Chi-Square Test using R is as follows:
chisq.test(V1, V2, data = dataframe)
For this, the
chisq.test() the function is used.