Glossary on Examine.com | Examine.com
The p-value is a number between 0 and 1 that indicates how surprising a study's results would be if there was actually no effect. The lower the value, the more reason you have to suspect the results are "real", as long as the study and statistical analyses are well-designed.
Science is all about testing hypotheses. You start with some idea about how some aspect of the world works, then you collect data to test your idea.
The starting hypothesis that an intervention (a diet, for instance, or a supplement) will make no difference, when compared to a placebo or some other control, is called a null hypothesis. Null hypotheses are the most common type of hypothesis scientists put to the test.
In this article, we are focusing almost entirely on a single core assumption needed when calculating a p-value: that the intervention makes no difference. As we said in the text, a null hypothesis presumes that the intervention being studied has no effect. However, this isn’t the only assumption that needs to be made to compute a p-value — how the data behave, how the data were collected, and what choices were made in analyzing and presenting the data all play a role. And if any of these assumptions are wrong, the p-value can be misleading.
Let’s say you test creatine against a placebo. The null hypothesis isn’t that creatine will increase strength more than the placebo will, but that it won’t. If your experiment nevertheless shows that creatine outperforms the placebo (or vice versa), this positive result goes against your null hypothesis, and so you are “surprised”.
