Today our goal is to cover hypothesis testing and the basic z-test, as these are fundamental to understanding how the t-test works. We’ll return to the t-test soon — with real data.

The (modest) goal of hypothesis testing is to reduce the directly-relevant data to a “level of suspicion” based purely on the data. That level of suspicion can then be combined (outside of hypothesis ...

Learn how two-tailed tests determine statistical significance in hypothesis testing by evaluating if a sample differs from a population mean. Discover real-world applications.

A statistical hypothesis test for a difference between the spatial distributions of two populations is presented. The test is based upon a generalization of the two-sample Cramér-von Mises test for a ...

Current scientific techniques in genomics and image processing routinely produce hypothesis testing problems with hundreds or thousands of cases to consider simultaneously. This poses new difficulties ...