Hypothesis testing
Content
- 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
Readings
- Chapter 5 (Foundations for inference) of Matthew Crump’s course notes. These notes are non-technical, but do a good job at explaining the notion of sampling variability and chance. If you find them too basic, skip directly to the next item.
- Chapter 2 of the Course notes
- The permutation test by Jared Wilson
Complementary readings
Slides
View all slides in new window Download PDF of all slides
Tip
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
Code
The following code reproduces the applications in the course slides and the case study
- R script
- SPSS scripts
- Python script for the case study
- Quarto report
References
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