Attempting to break down hypothesis testing using examples and Python’s SciPy library.
In statistics and data analysis, hypothesis testing is very important because when we perform experiments, we typically do not have access to all members of a population so we take samples of measurements to make inferences about the population. These inferences are hypotheses. In essence, a statistical hypothesis test is a method for testing a hypothesis about a parameter in a population using data measured in a sample.
In this article, I will be reviewing the steps in hypothesis testing, define key terminology and use examples to show the different types of hypothesis tests.
Regardless of the type of statistical hypothesis test you are performing, there are five main steps to executing them:
- Set up a null and alternative hypothesis
- Choose a significance level α (or use the one assigned)
- Determine the critical test statistic value or p-value (Find the rejection region for the null hypothesis)
- Calculate the value of the test statistic
- Compare the test statistic value to the critical test statistic value to reject the null hypothesis or not
Let’s break these down and define some key terminology.
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