Hypothesis testing is used to test whether certain assumptions, or premises, about a dataset could not happen by chance. If this is the case, then the results of the test are considered to be statistically significant.

Performing hypothesis testing is not a simple task. There are many different pitfalls to avoid such as the placebo effect or the observer effect. In the former, a participant will attain a result that they think is expected. In the observer effect, also called the Hawthorne effect, the results are skewed because the participants know they are being watched. Due to the complex nature of human behavior analysis, some types of statistical analysis are particularly subject to skewing or corruption.

The specific methods for performing hypothesis testing are outside the scope of this book and require a solid background in statistical processes and best practices. Apache Commons provides a package, org.apache.commons.math3.stat.inference, with tools for performing hypothesis testing. This includes tools to perform a student's T-test, chi square, and calculating p values.