Suppose two samples X and Z are dependent and of equal length. I see two approaches to investigate the possible difference in means:
1) Use a paired t-test if the normality assumption is met for each sample.
2) Subtract the samples from each other and apply a one-sample t-test to test whether the difference (X-Z) differs from 0.
Is there a difference in the way the test statistics are calculated? This thread (Paired difference t-test vs independent two sample t-test to assess means difference) with an excellent answer suggests there is not.
I confess, I often give in to using the test that suits me best for reporting. When both samples approximate normality I use a paired t-test, when they do not but the difference (X-Y) does I use a one-sample t-test. Is the latter approach wrong? In the proposed should situation should I always favour option (1) over option (2)?
Best Answer
To answer my own question (community wiki, feel free to adjust), after giving it some more thought and researching a bit:
A: No. I suppose @Glen_b was hinting in that direction. T-statistic remains the same, as do the DF.
A: No, the former is wrong. As can be read here (http://www.biostathandbook.com/pairedttest.html): The paired t–test assumes that the differences between pairs are normally distributed. Performing a t-test without checking the normality of the difference is wrong, regardless of the normality of X or Z (the constituents of the difference).
A: Either options work but normality of the difference needs to be verified in either case. You might as well do the one-sample t-test because you need to calculate the difference anyhow.