# What lowers the p-value besides the sample size?

The title pretty much says it. Sorry, it's so unspecific, I am not asking the question for myself and I am not familiar with stats.

Maybe that is already subsumed in Peter Flom's definition of the effect size, but a higher signal (for me, that is the effect size) to noise ratio: for example, in a regression context, your t-statistic would be $$t=\frac{\hat\beta_j-\beta_{j0}}{s.e.(\hat\beta_j)}$$ where $s.e.(\hat\beta_j)$ is the $j$th diagonal element of $s^2(X'X)^{-1}$. Here, $s^2$ is an estimator of $\sigma^2$, the error variance. A larger $\sigma^2$ will, all else equal, tend to produce larger $s^2$ and thus translate into a smaller t-statistic and hence a larger $p$-value.