The key with this is that there really isn’t much of a discrepancy.
In the first example, they’re clearly saying “inconclusive”.
In the second case, you fail to reject H0, which really is “inconclusive”.
Poor teaching of significance testing misleads students to think p > alpha means the null is true (in this case that no serial correlation exists), which just isn’t true. p > alpha means we don’t have enough evidence against the null to reject it at some pre specified alpha level. In our problem, there is some evidence against the idea of no serial correlation which is the null hypothesis, but it doesn’t surpass the threshold at the .05 level and clearly not at the .01 level-- but both are kind of “inconclusive”.
Failure to reject the null does NOT doesn’t not constitute proof or suggestion of the null’s truth. This is really where the question boils down to-- the way significance testing works (framing a conclusion) doesn’t really change from test to test.
Edit: because I was a doofus and couldn’t decide between “doesn’t” and “does not” when typing, I wrote “…doesn’t not…” which changed the written meaning. The correct meaning is that fail to reject Ho does not constitute evidence for the null.
I incorrectly mistyped “doesn’t not” but I meant that FTR H0does not constitute proof of or suggestion of the null hypothesis. It sounds like you gathered correct meaning, but just be sure. Sorry for my mistake.
Also, the misunderstanding is something that the CFAI could clear up if they had half a brain in statistics; it’s unintuitive at first to someone learning the material, but someone claiming competency to teach the material should recognize this as elementary and key for understanding (aka CFAI sucks at stats).