4. Hypothesis testing

Now that we’ve covered descriptive statistics and are familiar with our statistical software, it’s time to turn to inferential statistics. Remember, we conduct inferential statistics because we often cannot collect data from an entire population. Therefore, we collect a sample to draw inferences about the population of interest.

One of the ways we make inferences is using hypothesis testing. We are going to be learning about Null Hypothesis Significance Testing (NHST), which means we test and make inferences about the null hypothesis (which we’ll learn about in more detail soon).

Regardless of the inferential statistic we are performing, hypothesis testing goes through the same basic set of procedures:

  1. Look at the data by examining the descriptive statistics and describing your hypotheses
  2. Check assumptions to ensure your data is satisfactory for performing the inferential statistic (or choosing the correct statistic depending on which assumptions are met)
  3. Perform the test by running the inferential statistic
  4. Interpret the results and make a decision about whether you reject or fail to reject the null hypothesis, write-up the results in APA format, and provide a visualization of the results.

Let’s go through each of these in turn, using a hypothetical example.

Check Canvas for a useful 2-page “cheat sheet” of the hypothesis testing 4-step process detailed here. It summarizes the info you will learn here and in the subseqeunt set of chapters. Print it and bring it with you to class! It’s something you can use every time you perform inferential statistics.