The figure below shows results for the two-sample t -test for the body fat data from JMP software. Figure 5: Results for the two-sample t-test from JMP software. The results for the two-sample t -test that assumes equal variances are the same as our calculations earlier. The test statistic is 2.79996. Testing equality of population variances. Under H0, H 0, the ratio F =S2 A/S2 B = 2.5/3.0 = 0.8333 F = S A 2 / S B 2 = 2.5 / 3.0 = 0.8333 is distributed according to Snedecor's F distribution with 5 − 1 = 4 5 − 1 = 4 numerator degrees of freedom (df) and 7 − 1 = 6 7 − 1 = 6 denominator df. [This distribution is also called the 'variance The short answer: Yes, you can perform a t-test when the sample sizes are not equal. Equal sample sizes is not one of the assumptions made in a t-test. The real issues arise when the two samples do not have equal variances, which is one of the assumptions made in a t-test. When this occurs, it’s recommended that you use Welch’s t-test The syntax of the Stata commands can be found in the Stata help file. Run the following commands in your Stata command prompt, and you will see many examples showing the usage of these commands. The syntax for sdtesti for testing the equality of variances for variable1 and variable2 is. /* for testing variance of two variables */ sdtest
If the hypothesis of equal variances is rejected, another version of the Student’s t-test can be used: the Welch test (t.test(variable ~ group, var.equal = FALSE)). Note that the Welch test does not require homogeneity of the variances, but the distributions should still follow a normal distribution in case of small sample sizes.
1) Perform a Shapiro-Wilk test to assess normality. 2) If the data is not normal, perform Levene's test of equal variance. If the data is normal, an F-test. 3) Perform a Mann-Whitney Test (Wilcoxon Test) to compare difference in means. Or alternatively, Welch's t-test if the data is normal. My concerns: vT0u.
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    • how to test for equal variance