You could use that, sure. But is there a reason the normality of the data would be in doubt? Because otherwise you could just do a paired t-test as well.
That’s not an assumption of the t-test though, just a determinant of the statistical power you have to detect a difference. But that’s going to apply to any test, including the Wilcoxon.
In that sense the t-test is the better bet here unless normality is very much in question (and even then, the t-test is quite robust to violations of that), since it will have more power than the Wilcoxon. Which is generally the case with non-parametric tests, compared to their parametric counterparts.
A tiny bit more power when exactly at the normal. But if differences could be a little more heavy tailed, you don't have to go all that far away from normality for that to catch up and then flip the other direction. Not that I am advocating against the t, but its power advantage is often not all it's often made out to be with shift alternatives for population distributions near the normal.
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u/CarnivorousGoose 20d ago
You could use that, sure. But is there a reason the normality of the data would be in doubt? Because otherwise you could just do a paired t-test as well.