This week covers:

• More statistics: hypothesis testing, effect sizes, and regression

Day 1

Hypothesis testing (cont'd)

• We reviewed hypothesis testing and power calculations on the whiteboard (see notes)

• We went over the quiz in Mindless Statistics by Gigerenzer about common misconceptions around testing and p-values

• Finish up last week's stats exercises

References

• Understanding Statistical Power and Significance Testing
• Calculating the power of a test
• The American Statistical Association's statement on p-values by Wasserstein & Lazar
• Inference by eye by Cumming and Finch
• Statistical tests, P values, confidence intervals, and power: a guide to misinterpretations by Greenland et al.
• The Insignificance of Significance Testing by Johnson
• The Insignificance of Null Hypothesis Significance Testing by Gill

Day 2

Field trip

• We took a field trip to the American Museum of Natural History to visit there AstroCom data science program!

Day 3

Effect sizes

• We talked about false discoveries and effect sizes on the whiteboard (see notes)

• Replicate and extend the results of the Google n-grams "culturomics" paper using the template here

References

• Why Most Published Research Findings Are False
• Felix Schönbrodt's blog post and shiny app on misconceptions about p-values and false discoveries
• Interpreting Cohen's d effect size
• The New Statistics: Why and How by Cummings
• A guide on effect sizes and related blog post

Day 4

Regression

• We introduced regression and derived the best-fit parameters for a simple linear model

• See slides for a high-level framing and notes for derivation

• Read Chapter 5 of Intro to Stats with Randomization and Simulation, do exercises 5.20, 5.29

• Read Section 3.1 of Intro to Statistical Learning, do Lab 3.6.2

• See if you can reproduce the table in ISRS 5.29 using the original dataset

References

• This interactive shiny app on manual model fitting
• Chapters 1 and 2 of Advanced Data Analysis from an Elementary Point of View

Day 5

Regression (cont'd)

• See this notebook on fitting and visualizing linear models and this notebook on model evaluation
• Read Sections 6.1 through 6.3 of Intro to Stats with Randomization and Simulation
• Do Exercises 6.1, 6.2, and 6.3, and use the original data set in babyweights.txt, taken from here, to reproduce the results from the book
• Read Sections 3.2 and 3.3 of Intro to Statistical Learning
• Do Labs 3.6.3 through 3.6.6

References

• A visualization of ordinary least squares regression
• The "Model Basics" and "Model Building" Chapters in R for Data Science (Chapters 18 and 19 in the print edition, Chapters 23 and 24 online)
• The modelr and tidymodels packages in R