Links to YouTube, Rumble, Bitchute, and Twitter inside each class.

Class 1: Logic teaser (blog, Substack).

Class 2: Quid Est Veritas? (blog, Substack).

Class 3: Belief & Faith & The Necessity of Philosophy (blog, Substack).

Class 4: The Return of Logic (blog, Substack).

Class 5: Induction & Intellection (blog, Substack).

Class 6: Probability’s Birth! (blog, Substack).

Class 7: Intuition Check (blog, Substack).

Class 8: What Probability Is 1 (blog, Substack).

Class 9: Nothing Has A Probability (blog, Substack).

Class 10: Mind Your Ps and Qs & Monty Hall (blog, Substack).

Class 11: Tuesday’s Child & Relevance (blog, Substack).

Class 12: Psychic Card Guessing & The Proportional Syllogism (blog, Substack).

OLD ORIGINAL CLASS: Many good links still!

  • New Paper! Reality-Based Probability & Statistics: Solving the Evidential Crisis (link)
  • New Paper! Everything Wrong With P-values Under One Roof (link)
  • New Paper! The Replacement For Hypothesis Testing (link)
  • Randomization Isn’t Needed — And Can Be Harmful (link)
  • Non-Empirical Confirmation Of Theories (link)
  • On True And False Theories (link)
  • Pick A Random Number From 1-10 (link)
  • Another Proof Statistics Cannot Discover Cause (link)
  • AI Is Kicking Statistics’s Ass (link)
  • An Argument Against The Multiverse (link)
  • Another Proof Against P-Value Reasoning (link)
  • Judea Pearl Is Wrong On AI Identifying Causality, But Right That AI Is Nothing But Curve Fitting (link)
  • Proof Cause Is In The Mind And Not In The Data (link)
  • The Controversy Over Randomization And Balance In Clinical Trials (link)
  • Parameters Aren’t What You Think (Here’s What They Are) (link)
  • JASA: The Substitute for P-Values (link)
  • Manipulating the Alpha Level Cannot Cure Significance Testing (link)
  • Quantum Potency & Probability (link)
  • Is Presuming Innocence A Bayesian Prior? (link)
  • There Is No “Problem” Of Old Evidence In Bayesian Theory (link)
  • There Is No Prior? What’s A Bayesian To Do? Relax, There’s No Model, Either (link)
  • How To Resolve All Probability Paradoxes: Apples In Sack Example (link)
  • P-values vs. Bayes Is A False Dichotomy (link)
  • Signal + Noise vs. Signal (link)
  • What Neural Nets Really Are (link)
  • Every Result Of Unsupervised Learning Is Correct; Or, All Learning Is Supervised (link)
  • Everything Is Already In The Simulation (or the model or theory) (link)
  • Making Random Draws Is Nuts (link)
  • The Gremlins Of MCMC: Or, Computer Simulations Are Not What You Think (link)
  • The Hierarchy Of Models: From Causal (Best) To Statistical (Worst) (link)
  • The Solution To The Doomsday Argument (link)
  • Real Versus Statistical Control (link)
  • Formal Logic And Probability (link)
  • Bayesian Statistics Isn’t What You Think (link)
  • Falsifiability Is Not That Useful (link)
  • The Difference Between Essential And Empirical Models (link)
  • Under-determination, Quus, And Why It’s Cause That Counts (And With A Taste Of Grue) (link)


  1. How To Do Predictive Statistics: Part I: Introduction: MUST READ (link)
  2. How To Do Predictive Statistics: Part II: Regression 1 (link)
  3. How To Do Predictive Statistics: Part III: Regression 2 (link)
  4. How To Do Predictive Statistics: Part IV: Logistic Regression (link)
  5. How To Do Predictive Statistics: Part V: Multinomial Regression (link)
  6. How To Do Predictive Statistics: Part VI: Poisson Regression (link)
  7. How To Do Predictive Statistics: Part VII: Tobit Regression (link)
  8. How To Do Predictive Statistics: Part VIII: Starting Stan regression (link)
  9. How To Do Predictive Statistics: Part IX: Logistic & Beta Regression (link)
  10. How To Do Predictive Statistics: Part X: Survival Analysis (link)
  11. New! How To Do Predictive Statistics: Part X: Verification 1 (link)
  1. Choose Predictive Over Parametric Every Time (link)
  2. The Solution To The Doomsday Argument (link)
  3. Falsifiability Is Falsifiable (link)
  4. A Beats B Beats C Beats A (link)
  5. Quantum Potency & Probability (link)
  6. Against Moldbug’s Reservationist Epistemology: Reason Alone Is Not Reasonable (link)

Free Software!: mcmc.pred.R, mcmc.pred.examples.R. Data are linked in individual posts.

Applied Data Science/Statistics/Applied Probability. Free on-line class general link.