Note the pooled SD is for both data set columns for all rows. This gives you more degrees of freedom and thus more power. Prism therefore computes one pooled SD, as it would by doing two-way ANOVA. This is the assumption of homoscedasticity. But the assumption is that this variation is random, and really all the data from all rows comes from populations with the same SD. You assume that all the data from both columns and all the rows are sampled from populations with identical standard deviations. This is the standard assumption of an unpaired test - that the two samples being compared are sampled from populations with identical standard deviations. Note that while you are not assuming that data on different rows are sampled from populations with identical standard deviations, you are assuming that data from the two columns on each row are sampled from populations with the same standard deviation. There are fewer df, so less power, but you are making fewer assumptions. The values in the other rows have nothing at all to do with how the values in a particular row are analyze. With this choice, each row is analyzed individually. There are two ways it can do this calculation. Prism computes an unpaired t test for each row, and reports the corresponding two-tailed P value.
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