Data All The Way
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  • Cheat sheets
  • Data processing
  • R
  • Statistics
  • Machine learning

Bookmarks

Below are some of the articles, blog posts, and stack exchange threads that I have found helpful. Please comment if you know or found something interesting that I should list here.

Cheat sheets

  • Choosing the right estimator
  • Machine learning cheat sheet
  • Machine learning glossary
  • The neural network zoo

Data processing

  • Normalize data before or after split of training and testing data?

R

  • Book: Advanced R by Hadley Wickham
  • Teaching R in a Kinder, Gentler, More Effective Manner: Teach Base-R, Not Just the Tidyverse Author: Prof. Norm Matloff, University of California, Davis
  • R Workflow - An overview of R Workflow, which covers how to use R effectively all the way from importing data to analysis, and making use of Quarto for reproducible reporting.

Statistics

  • The ASA Statement on p-Values: Context, Process, and Purpose
Statistical significance is not equivalent to scientific, human, or economic significance

Statistical significance is not equivalent to scientific, human, or economic significance. Smaller p-values do not necessarily imply the presence of larger or more important effects, and larger p-values do not imply alack of importance or even lack of effect… No single index should substitute for scientific reasoning.

  • Statistical Inference in the 21st Century: A World Beyond p < 0.05
Don’t Say “Statistically Significant”

The ASA Statement on P-Values and Statistical Significance stopped just short of recommending that declarations of “statistical significance” be abandoned. We take that step here. We conclude, based on our review of the articles in this special issue and the broader literature, that it is time to stop using the term “statistically significant” entirely. Nor should variants such as “significantly different,” “p < 0.05,” and “nonsignificant” survive, whether expressed in words, by asterisks in a table, or in some other way.

Regardless of whether it was ever useful, a declaration of “statistical significance” has today become meaningless. Made broadly known by Fisher’s use of the phrase (1925), Edgeworth’s (1885) original intention for statistical significance was simply as a tool to indicate when a result warrants further scrutiny. But that idea has been irretrievably lost. Statistical significance was never meant to imply scientific importance, and the confusion of the two was decried soon after its widespread use (Boring 1919). Yet a full century later the confusion persists.

  • Bayesian and frequentist reasoning in plain English

  • Ultimate Guide to Statistics for Data Science

  • Book: Improving Your Statistical Inferences

  • Book/course: Online Statistics Education: An Interactive Multimedia Course of Study

  • Book: Bayesian Data Analysis Third edition by Andrew Gelman, John Carlin, Hal Stern, David Dunson, Aki Vehtari, and Donald Rubin.

  • Book: Regression and Other Stories by Andrew Gelman, Jennifer Hill, Aki Vehtari

Machine learning

  • A free deep learning course
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