**P-value cutoff:**The stringency of your statistical test. This sets the False Positive Rate.**Statistical power:**The statistical power of your test. This is the probability of a significant result at the given P-value cutoff if there really is a difference (*e.g.*between treatment and control) of the desired size. This sets the False Negative Rate.**Real % chance of difference:**The real world determines the*a priori*likelihood that your experiment*should*have a positive (“significant”) result,*i.e.*the null hypothesis is not true.

Table above calculates the different possible outcomes based on these three parameters. The **Correct** and **Incorrect** columns give you the probability of being correct if you trust the result of a statistical test in this scenario.

Interpreting p-values is more complex than at first it appears. A p-value tells you the **False Positive Rate** of your statistical test *if the null hypothesis is true*. In other words, you only expect the actual False Positive Rate to match the p-value if there is no chance of there being a real effect. If you expect a real effect, your interpretation of p-values changes. This tool is designed to help with this intepretation. In reality, you probably do not know this number, in which case this tool is a cautionary tale regarding the importance of this unknown aspect of your experiment.

The questions researchers really want to answer are when presented with a p-value are:

- What proportion of “significant” results represent a real effect? (True Positives)
- What proportion of “non-significant” results should have actually been significant? (False Negatives)

These values can be found in the **Correct** and **Incorrect** columns.

See the GraphPad Statistics Guide for some example scenarios.