A beginner’s guide to statistical hypothesis tests

A simple introduction to hypothesis tests

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Statistics is a data scientist’s best friend. Hypothesis tests are a family of very useful tools for any kind of analysis. They can really help us assess the statistical significance of some phenomena. However, we have to master such tools properly in order to benefit from their advantages.

What is a hypothesis test?

A hypothesis test is a statistical test that assesses a particular hypothesis (called the “null” hypothesis) and calculates confidence about it. For example, if we calculate the height of Mount Everest (which we know to be 8848 meters) and we get 8845 meters, we would like to assess whether the difference between our measure and the known result is statistically significant. That’s the purpose of hypothesis tests. They make us calculate a confidence level and apply it to a particular hypothesis.

Here are the ingredients of a typical hypothesis test:

• The null hypothesis (e.g. our measure of Mount Everest height is statistically equivalent to the known one)
• A statistic value, that is a number calculated by the test and depends on the test type
• A p-value, that is the probability of having as extreme statistics…