Statistics are the useful numbers that get called upon in any number of situations. Sadly they're often misused and abused mainly by politicians, spin-doctors and the media who have an agenda. Statisticians per se are a fairly honest bunch (at least those I've met, and I'm including myself in that!).

• Variance
• Sample Size and Clustering
• Problems with Step-wise Procedures
• Baseline
• Nomenclature
• Interpretation
• Dichotomisation and problems there-in
• The Folly of Frequentism v Bayesianism
• Problems with a little p
• Data

• Nomenclature
• Logistic Regression
• Linear Regression
• Generalised Linear Model
• Nonlinear Models
• LASSO
• Model interpretation

• Data Visualisation Resources
• Colour Palettes

Worthy of its own page.

I used to use Stata and the Open Source R, but these days I also use Python. Increasingly these days Stata sees little look in as I've become quite enamoured with the R ecosystem, but will often cross-validate work done in one package by repeating it in the other or revert to Stata if something isn't available in R (increasingly rare). Whilst statistical packages are great at analysing data databases are far more suited to the task, particularly when there is a large amount of data and multiple tables and I've opted to use PostgreSQL. Detailed below are tips and tricks on getting things done in these software packages.

Notes on using either package can be found at…

• Postgresql
• Stata
• r

• Stata
• Statalist
• R
• CRAN
• Emacs Speaks Statistics

• OpenScienceFoundation
• Openscapes
• protocols.io
• Cassyni

• Scientific Method, Statistical Method and the Speed of Light on JSTOR

• Understanding Statistics shows various statistical issues in a graphical manner (particularly useful for dichotomisation).
• Regression Diagnostics - John Fox
• Simpsons Paradox
• Shrinkage in Mixed Effects Models Visualised
• Classification vs. Prediction | Statistical Thinking
• machine learning - Why is accuracy not the best measure for assessing classification models? - Cross Validated
• Interpretable Machine Learning
• Reference Collection to push back against "Common Statistical Myths"
• Overfitting: a guided tour

• Markov Chains

• PAC-Bayes: Cosmos — All that is, or was, or ever will be
• Probably approximately correct: Cosmos — All that is, or was, or ever will be

• Visualizations | R Psychologist interactive graphs demonstrating statistical principles.
• Interactive visualization of Gaussian processes
• A Visual Exploration of Gaussian Processes
• What to consider when using text in data visualizations

• Deep learning generalizes because the parameter-function map is biased towards simple functions
• PNAS | The Science of Deep Learning

• LIME : Why should I trust you?" Explaining the Predictions of Any Classifier
• Python LIME: Explaining the predictions of any machine learning classifier
• R LIME
• SHAP

• Amazon Machine Learning University
• Generalized Additive Models in R · A Free Interactive Course
• Statistical Rethinking course winter 2022

• RA Fisher Digital Archive
• Edward Tufte : Proponent of clear presentation styles, particularly with graphs.
• Handy statistical lexicon « Statistical Modeling, Causal Inference, and Social Science from Andrew Gelman.

• Five rules for evidence communication
• Statcheck detecting errors in published results.

• Dataset Search
• Breast Cancer MRI Dataset – Maciej A. Mazurowski, Ph.D. Direct : Dynamic contrast-enhanced magnetic resonance images of breast cancer patients with tumor locations (Duke-Breast-Cancer-MRI) - The Cancer Imaging Archive (TCIA) Public Access - Cancer Imaging Archive Wiki

• 9 Great Statistics papers published after 1970 Rob Tibshirani

• rspatialdata

* "Why Should I Trust You?" Explaining the Predictions of Any Classifier

These can be used to generate new images such as faces, MPs, dicks…

• This Person Does Not Exist
• This Cat Does Not Exist
• This Chemical Does Not Exist
• This MP Does Not Exist
• TDPDNE (NSFW!!!)
• A collection of This * Does Not Exist websites.
• This X Does Not Exist

• Online Library and Publication Platform | Directory of Open Access Books
• Statistical Learning with Sparsity: The Lasso and Generalizations
• An Introduction to Statistical Learning same material using the tidymodels ISLR tidymodels Labs
• Elements of Statistical Learning
• Computer Age Statistical Inference: Algorithms, Evidence and Data Science
• Bayesian Data Analysis
• Ecological Models and Data
• Statistical Analysis Handbook which is particularly useful for its PPDAC section.
• Practical Deep Learning Book - Practical Deep Learning Book
• Tidy Modeling with R
• Seeing Theory
• Causal Inference Book | Miguel Hernan
• Introduction to Tensor Calculus
• Feature Engineering and Selection: A Practical Approach for Predictive Models
• Data Structures and Information Retrieval in Python
• Bayesian Modeling and Computation in Python
• Introduction to Multilevel Modelling

• R for Data Science
• Advanced R
• R Markdown: The Definitive Guide
• Bayes Rules! An Introduction to Applied Bayesian Modeling
• Regression Modelling Strategies
• Reproducible Analytical Pipelines - Master’s of Data Science

• Think Bayes 2 — Think Bayes
• Probability and Bayesian Modeling (Probability and Bayesian Modeling | ProbBayes)

• Issac Newton Institute Podcast
• Risky Talk

statistics R stata ess howto tips programming