T Test Vs Chi Square. When you reject the null hypothesis with a t test you are saying that the means are statistically different. A t test is designed to test a null hypothesis by determining if two sets of data are significantly different from one another while a chi squared test tests the null hypothesis by finding out if there is a relationship between the two sets of data.
The null hypothesis is a prediction that states there is no relationship between two variables. When you reject the null hypothesis of a t test for a difference in means it means the two population means are not equal. The t test is an inferential.
In contrast to the chi square values which result from squared differences the residuals are not squared.
Learning statistics doesn t need to be difficult. When you reject the null hypothesis with a t test you are saying that the means are statistically different. The t test is an inferential. A t test is designed to test a null hypothesis by determining if two sets of data are significantly different from one another while a chi squared test tests the null hypothesis by finding out if there is a relationship between the two sets of data.
