Hypothesis testing


  • Sampling variability
  • Hypothesis testing
  • Pairwise comparisons

Learning objectives

At the end of the session, students should be capable of

  • understanding the mechanics behind generic hypothesis tests
  • interpreting the output of generic tests
  • correctly reporting the output of a testing procedure


Complementary readings

  • Chapter 2 of Maxwell et al. (2017)
  • Chapter 3 of Keppel & Wickens (2004).


View all slides in new window Download PDF of all slides


Fun fact: If you type ? (or shift + /) while going through the slides, you can see a list of slide-specific commands.

Case study

We will look at the way authors report the conclusion of their statistical tests with

  • Rosen & Jerdee (1974)
  • Brucks & Levav (2022)
  • Liu et al. (2023), Experiment 1


The following code reproduces the applications in the course slides and the case study


Brucks, M. S., & Levav, J. (2022). Virtual communication curbs creative idea generation. Nature, 605(7908), 108–112. https://doi.org/10.1038/s41586-022-04643-y
Keppel, G., & Wickens, T. D. (2004). Design and analysis: A researcher’s handbook. Pearson Prentice Hall.
Liu, P. J., Rim, S., Min, L., & Min, K. E. (2023). The surprise of reaching out: Appreciated more than we think. Journal of Personality and Social Psychology, 124(4), 754–771. https://doi.org/10.1037/pspi0000402
Maxwell, S. E., Delaney, H. D., & Kelley, K. (2017). Designing experiments and analyzing data: A model comparison perspective (3rd ed.). routledge. https://doi.org/10.4324/9781315642956
Rosen, B., & Jerdee, T. H. (1974). Influence of sex role stereotypes on personnel decisions. Journal of Applied Psychology, 59, 9–14. https://doi.org/10.1037/h0035834