A t test compares the means of two groups. There are several types of two sample t tests and this calculator focuses on the three most common: unpaired, welch's, and paired t tests. Directions for using the calculator are listed below, along with more information about two sample t tests and help on which is appropriate for your analysis.
12.1 - One Variance. Yeehah again! The theoretical work for developing a hypothesis test for a population variance σ 2 is already behind us. Recall that if you have a random sample of size n from a normal population with (unknown) mean μ and variance σ 2, then: χ 2 = ( n − 1) S 2 σ 2. follows a chi-square distribution with n −1 degrees
\(F\)-Tests for Equality of Two Variances. In Chapter 9 we saw how to test hypotheses about the difference between two population means \(μ_1\) and \(μ_2\). In some practical situations the difference between the population standard deviations \(σ_1\) and \(σ_2\) is also of interest. Standard deviation measures the variability of a random
Equal Variance Assumption in t-tests. A two sample t-test is used to test whether or not the means of two populations are equal. The test makes the assumption that the variances are equal between the two groups. There are two ways to test if this assumption is met: 1. Use the rule of thumb ratio.
The F-test for variances takes the ratio of the sample variances: F = S2X S2 Y F = S X 2 S Y 2. So you see that if Y Y is the one group with the identical values (low variance) it is not defined and if X X (zero=low variance) it is zero (test failure). So, by definition, the larger variance should be placed in the numerator.
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how to test for equal variance
