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T Test With Unequal Sample Sizes. First ensure that your data pass a test of homoscedasticity–are the variances homogenous. 2 Sample 2 tail 1 2 Sample t-Test unequal sample sizes and unequal variances Like the last example below we have ceramic sherd thickness measurements in cm of two samples representing different decorative styles from an archaeological site. This is commonly known as the Aspin-Welch test Welchs t-test Welch 1937 or the Satterthwaite method. How do the results of your t-test differ if you use all of the data again assuming that each population has the same variance.
Thats the very reason to use the Welch test. With equal sample sizes you could just use the t-test regardless of the unequal variances. We do this in R with a Fishers F-test vartestx y. Variations of the t-Test. A paired t-test when you have unequal sample sizes does not make any sense conceptually or mathematically. If the variances are equal then the equal and unequal variances versions of the t-test will yield similar results even when the sample sizes are unequal although the equal variances version will have slightly better statistical power.
If the sample sizes in the two groups being compared are equal Students original t -test is highly robust to the presence of unequal variances.
A paired sample t-test for unequal sample sizes. A paired sample t-test for unequal sample sizes. If the variances are equal then the equal and unequal variances versions of the t-test will yield similar results even when the sample sizes are unequal although the equal variances version will have slightly better statistical power. A sample of archaic Homo sapiens n 65 and a sample of H. OK we actually have a lot more data for surviving than dead horned lizards. If the sample sizes in the two groups being compared are equal Students original t -test is highly robust to the presence of unequal variances.