Workshop Schedule

P-values, “significance,” and the view from data science

1. Welcome, introductions and orientation

2. P-values. Supporting readings
   • TAS
     
   • Nature “Retire significance” editorials.
   • Nature Methods 2016,
   • Nature “Five ways to fix statistics”,
   • Nature editorial

3. Instructor technology to support our work here and provide good tools for data science. Part 1: Installation. RStudio Cloud space, which you will clone. After the break, you’ll use your own clone rather than the preceding link.

• 3:00 to 3:30 break

3. Instructor technology orientation. Remember to use your own clone of the workshop project.

4. While we’re at it, what things should we change in intro stats?
   • What we don’t like discussion group notes - backup notes
   • Topics for a new intro stats moving to Thursday?
   • Anthem

• 5:00 adjourn for the day

Technology encore: three levels of computing

1. Little Apps & the StatPREP 101 lessons

   devtools::install_github("StatPREP/LittleApp") # Say "none" when asked about updating packages
   # ... then, when installation is finished ...
   LA_run("bootstrap")

2. R tutorials:

   • orientation to the grammar of the language:

     learnr::run_tutorial("SDS-language",  package = "SDStutorials")

   • Using R functions:

     learnr::run_tutorial("SDS-functions",  package = "SDStutorials")

3. Rmd projects on RStudio.cloud

Stats for data science

1. Objectives
2. Data
   • R tutorials: "SDS-data"
3. Graphics
   • R tutorial: "SDS-graphics"
4. Summary & prediction
   • R tutorial: "SDS-prediction"

-12:00 - 1:30pm – break for lunch

5. Stratification
   • R tutorial: "SDS-stratification"
6. Process of investigation
7. Case study: from purpose to result
8. Bayes’ rule
   • R tutorial: "SDS-bayes"

• 3:00 - 3:20 break

9. Model functions
   • R tutorial: "SDS-modeling-functions"
   • R tutorial: "SDS-prediction-intervals"
   • Exercises
10. Models that learn
    • R tutorials: "SDS-models-that-learn"
    • Exercises:
11. Effect size
    • R tutorials: "SDS-effect-size"
12. Causal networks
13. Simulation

• 5:00pm – adjourn