Because it never turns out the way I want it, that confounded thing!
Seriously, the p value is literally a confounded index because it reflects both the size of the underlying effect and the size of the sample. Hence any information included in the p value is ambiguous (Lang et al. 1998).
Consider the following equation, which comes from Rosenthal and Rosnow (1984):
Statistical significance = Effect size x Sample size
Now let’s hold the effect size constant for a moment and consider what happens to statistical significance when we fiddle with the sample size (N). Basically, as N goes up, p will go down automatically. It has to. It has absolutely no choice. This is not a question of careful measurement or anything like that. It’s a basic mathematical equation. The bigger the sample, the more likely the result will be statistically significant, regardless of other factors.
Conversely, as N goes down, p must go up. The smaller the sample, the less likely the result will be statistically significant.
So if you happen to get a statistically significant result (a low p value), it could mean that (a) you have found something, or (b) you found nothing but your test was super-powerful because you had a large sample.
Researchers often confuse statistical significance with substantive significance. But smart researchers understand that p values should never be used to inform judgments about real world effects.