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If my difference scores are not normally distributed - and I want to do a parametric paired t-test - do I:

  1. log transform the the original scores and perform a paired t-test on these scores
  2. log transform the difference scores and do a one sample t-test against a test value of 0
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For a paired test, what is relevant is the distribution of the difference scores, not the individual distributions. Even if the original scores have a nonnormal distribution (difficult to say with only $n=17$), the difference might be normal (or at least symmetric). So I would have first a look at the qqplot of the differences against a normal distribution. Then, if necessary, transform the difference score. Or use a nonparametric procedure. More discussion in the related post at Skewness transformation for one but not the other variable?

An alternative could be a permutation test (permuting the signs of the absolute differences), which do not depend on distribution assumptions. For further discussion of alternatives see Best practice when analysing pre-post treatment-control designs

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I would also consider what assumptions you can make about your data, particularly whether there is true independence of the groups being compared. This is a key underlying assumption in t-tests. Whether or not you meet the assumptions will govern which test you use.

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