![]() Which follows an $F$-distribution, will be large (i.e., somewhere in the right tail of the distribution). When at least one mean is significantly different from the others, the variance of the sample means will be larger, relative to the mean of the sample variances.Ĭonsequently, precisely when at least one mean is significantly different from the others, the ratio of these estimates When the means are not significantly different, the variance of the sample means will be small, relative to the mean of the sample variances. The strategy behind an ANOVA test relies on estimating the common population variance in two different ways: 1) through the mean of the sample variances - called the variance within samples and denoted $s^2_w$, and 2) through the variance of the sample means - called the variance between samples and denoted $s^2_b$. In the case where one is dealing with $k \ge 3$ samples all of the same size $n$, the calculations involved are much simpler, so let us consider this scenario first. ![]() The null hypothesis is that all population means are equal, the alternative hypothesis is that at least one mean is different.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |