What is the difference between chi squared and t test

Improve this answer. Sheridan Grant Sheridan Grant 4 4 silver badges 13 13 bronze badges. Ok, that's enough harping on categorization. Demetri Pananos Demetri Pananos 22k 1 1 gold badge 34 34 silver badges 85 85 bronze badges. Alexis For non-normal data, it is possible for the rank sum test to have more power to reject than a t test. Moreover, these tests have different null hypotheses, and the CLT does not get you around the fact that what is being inferred is different for each test.

The rank sum test strongly assumes that the data differ in mean and only in mean. And this is extremely sensitive to any differences in the distributions other than location shifts.

It is very general, and often a good choice when you want to test for a difference in means. Show 3 more comments. Sign up or log in Sign up using Google. Sign up using Facebook. Sign up using Email and Password. Post as a guest Name. Email Required, but never shown. Featured on Meta. Now live: A fully responsive profile.

Version labels for answers. Linked 1. We measure the height of random students from one school and random students from another school. We can conduct a t-test for a difference in means to determine if there is a statistically significant difference in average height of students between the two schools.

Before we can conduct a hypothesis test for a difference between two population means, we first need to make sure the following conditions are met to ensure that our hypothesis test will be valid:. If these assumptions are met, then we can then conduct the hypothesis test.

Chi-Square Test for independence: Allows you to test whether or not not there is a statistically significant association between two categorical variables.

When you reject the null hypothesis of a chi-square test for independence, it means there is a significant association between the 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 easiest way to know whether or not to use a chi-square test vs. If you have two variables that are both categorical, i.

But if one variable is categorical e. Your email address will not be published. Skip to content Menu. Posted on June 23, May 10, by Zach. Chi-Square Test There are actually a few different versions of the chi-square test, but the most common one is the Chi-Square Test of Independence.

Definition We use a chi-square test for independence when we want to formally test whether or not there is a statistically significant association between two categorical variables.

The hypotheses of the test are as follows: Null hypothesis H 0 : There is no significant association between the two variables. Examples Here are some examples of when we might use a chi-square test for independence: Example 1: We want to know if there is a statistically significant association between gender male, female and political party preference republican, democrat, independent. Assumptions Before we can conduct a chi-square test for independence, we first need to make sure the following assumptions are met to ensure that our test will be valid: Random: A random sample or random experiment should be used to collect the data for both samples.

Categorical: The variables we are studying should be categorical. A chi-square test tests a null hypothesis about the relationship between two variables. For example, you could test the hypothesis that men and women are equally likely to vote "Democratic," "Republican," "Other" or "not at all. A t-test requires two variables; one must be categorical and have exactly two levels, and the other must be quantitative and be estimable by a mean.

For example, the two groups could be Republicans and Democrats, and the quantitative variable could be age. A chi-square test requires categorical variables, usually only two, but each may have any number of levels. There are variations of the t-test to cover paired data; for example, husbands and wives, or right and left eyes.

There are variations of the chi-square to deal with ordinal data — that is, data that has an order, such as "none," "a little," "some," "a lot" — and to deal with more than two variables. The t-test allows you to say either "we can reject the null hypothesis of equal means at the 0.

Peter Flom is a statistician and a learning-disabled adult.