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:
These values can be found in the Correct and Incorrect columns.
See the GraphPad Statistics Guide for some example scenarios.