We can also carry out the t-test for Example 1 by using the following Excel data analysis tool. Excel Data Analysis Tool: Select Data > Analyze|Data Analysis and then choose the Two-Sample Assuming Equal Variances option from the dialog that appears. Next, fill in the dialog box that appears as shown in Figure 2.
We use this test to measure if two group samples are statistically independent of each other. This test enables us to establish if the two population means are equal or not. There are two types of Two Sample T-Hypothesis tests: Equal Variance: The two populations share equal variances. Unequal Variance: The two populations share unequal variances.
ANOVA tests whether any of the group means are different from the overall mean of the data by checking the variance of each individual group against the overall variance of the data. If one or more groups falls outside the range of variation predicted by the null hypothesis (all group means are equal), then the test is statistically significant.
Two Sample t-Test: Equal vs Unequal Variance Assumption: Learn about the assumption of equal variance (or standard deviation) vs non-equal variance (or stand
A paired t-test is designed to compare the means of the same group or item under two separate scenarios. An unpaired t-test compares the means of two independent or unrelated groups. In an unpaired t-test, the variance between groups is assumed to be equal. In a paired t-test, the variance is not assumed to be equal.
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$\begingroup$ 'variances equal' simply means that the population variance for one thing is the same as the population variance for some other thing or things. The distribution of the variance is restricted to the non-negative half of the real line - so variances can't be normal, except in a limiting sense (a variance is a kind of average, and the CLT will apply to it if the usual CLT . 7 262 161 316 357 264 126 241 148

how to test for equal variance