T Test And Chi Square Test Difference. Moreover when there is a standard deviation given and the sample size is large on the other hand chi square is a procedure used for testing if two. A chi square fit test for two independent variables is used to compare two variables in a contingency table to check if the data fits.
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The difference is meaningful. T test allows you to differentiate between the two groups. When you reject the null hypothesis of a chi square test for independence it means there is a significant association between the two variables.
A high chi square value means that data doesn t fit.
The t test is an inferential. But it does not tell you the direction or the size of the relationship. The null hypothesis is a prediction that states there is no relationship between two variables. Allows you to test whether or not there is a statistically significant difference between two population means.
