What Is the Difference Between a T-Test and a Chi-Squared Test?
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.
A student can use a one-sample t-test or a two-sample t-test. A one-sample t-test is designed to answer a null hypothesis that concerns the data set’s mean when the data are from independent observation and follow a normal distribution.
The two-sample t-test evaluates the null hypothesis when two sets of data are collected. Both sets of data must come from the same sample size for the results to be valid.
For a chi-squared test, the two sets of data must first be divided into categories. Once the data are divided, the chi-squared test is used to evaluate the two sets of data to see if there is a relationship between the figures.
To run a t-test or a chi-squared test, students can use a graphing calculator or a computer program. Though these aids run the tests, the students must input the correct data for accurate results.
