Machine Learning

How to Calculate Critical Values for Statistical Hypothesis Testing with Python

Last Updated on September 24, 2019

In is common, if not standard, to interpret the results of statistical hypothesis tests using a p-value.

Not all implementations of statistical tests return p-values. In some cases, you must use alternatives, such as critical values. In addition, critical values are used when estimating the expected intervals for observations from a population, such as in tolerance intervals.

In this tutorial, you will discover critical values, why they are important, how they are used, and how to calculate them in Python using SciPy.

After completing this tutorial, you will know:

  • Examples of statistical hypothesis tests and their distributions from which critical values can be calculated and used.
  • How exactly critical values are used on one-tail and two-tail statistical hypothesis tests.
  • How to calculate critical values for the Gaussian, Student’s t, and Chi-Squared distributions.

Kick-start your project with my new book Statistics for Machine Learning, including step-by-step tutorials and the Python source code files for all examples.

Let’s get started.