Machine Learning

A Gentle Introduction to Statistical Hypothesis Testing

Last Updated on April 10, 2020

Data must be interpreted in order to add meaning.

We can interpret data by assuming a specific structure our outcome and use statistical methods to confirm or reject the assumption. The assumption is called a hypothesis and the statistical tests used for this purpose are called statistical hypothesis tests.

Whenever we want to make claims about the distribution of data or whether one set of results are different from another set of results in applied machine learning, we must rely on statistical hypothesis tests.

In this tutorial, you will discover statistical hypothesis testing and how to interpret and carefully state the results from statistical tests.

After completing this tutorial, you will know:

  • Statistical hypothesis tests are important for quantifying answers to questions about samples of data.
  • The interpretation of a statistical hypothesis test requires a correct understanding of p-values and critical values.
  • Regardless of the significance level, the finding of hypothesis tests may still contain errors.

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