===== Statistics 101 =====

Goal: //Review of core statistics for psychophysiology research//

==== Prepare for the lab ====

  * Recall basic statistical concepts (from high school or from statistics at university or from Wikipedia: [[wp>Statistics]] and [[wp>Statistical_hypothesis_testing]]). As a guideline, you can follow the keywords included with the Q&A session below.

==== Materials ====

  - Q&A Session -- a short series of keywords to "warm up". Describe (in a few words) what are:
    - Null hypothesis and Alternative hypothesis (and how they change when the t-test is one-tailed or two-tailed)
    - Normal distribution
    - Significance level (alpha)
    - p-value
    - Type 1 and Type 2 errors
    - Student's t-test
    - Analysis of Variance (ANOVA)
    - Multiple comparisons
    - Post-hoc tests
  - Practice session:
    - Practice the basics of statistics by using Student's t-test and ANOVA to verify some statistical hypotheses in the Jupyter Notebook: [[https://colab.research.google.com/drive/1TWc7MCHocwaaAk7bNTpofmyqYzjkLR2L?usp=sharing|Statistics 101]]


==== Learn more! ====

  * **[[https://www.discoveringstatistics.com/statistics-hell-p/|Statistics Hell-P]] -- nice introduction into core statistical concepts**
  * [[https://www.kdnuggets.com/2019/07/p-values-explained-data-scientist.html|P-values Explained By Data Scientist]] -- how to understand the p-values
  * [[http://mattgolder.com/interactions|Interactions | Matt Golder]] -- a very detailed explanation of what interactions in linear models are and are not
  *   [[https://www.youtube.com/watch?v=YfNx42c4Vow| Different types of error rates | mumfordbrainstats]] -- explanation and visual comparison of PCER, FWER, and FDR for multiple comparisons problem in voxelwise testing of fMRI