This is the standard assumption of an unpaired test - that the two samples being compared are sampled from populations with identical standard deviations. You assume that all the data from both columns and all the rows are sampled from populations with identical standard deviations. But the assumption is that this variation is random, and really all the data from all rows comes from populations with the same SD. This is the assumption of homoscedasticity. Prism therefore computes one pooled SD, as it would by doing two-way ANOVA. This gives you more degrees of freedom and thus more power.
Note the pooled SD is for both data set columns for all rows. So the data in each row will influence the P value not only for that row, but also for every other row.Ĭhoosing between these options is not always straightforward. Certainly if the data in the different rows represent different quantities, perhaps measured in different units, then there would be no reason to assume that the scatter is the same in all.
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