In JMP, a p-value between .01 and .05 is colored red; less than .01 is orange; greater than .05 is black. Color-coding can help when you are analyzing a number of different effects at once and want to look in reports for visual cues of statistical significance.

P-values help us make inferences about populations from the sample we analyze. They relate to the null hypothesis - which is that there is no effect in the population from which a sample was taken.

In simple terms,a **low p-value **suggests that the null hypothesis is wrong, and that there really is an effect out there in the population.

For example, a sample of 50 individuals might show a correlation between two variables. The null hypothesis states that there is **no **correlation out there in the whole population, but a **low p-value** for the correlation suggests that the null hypothesis is wrong (i.e., there really is a correlation in the population). Thinking of **p** as standing for probability, one might think of a low p-value as indicating that the result we see in our sample would be **low probability** if there weren’t actually an effect out in the population.

Statisticians typically consider a p-value ≤ 0.05 statistically significant. In some cases, people look for p-values <.01.

Ross Metusalem @Ross_Metusalem explains: