William M. Briggs, PhD,
is Adjunct Professor of Statistics at Cornell University. Having earned both his PhD in Statistics and MSc in Atmospheric Physics from Cornell University, he served as the editor of the American Meteorological Society journal and has published over 60 papers. He studies the philosophy of science, the use and misuses of uncertainty - from truth to modeling. Early in life, he began his career as a cryptologist for the Air Force, then slipped into weather and climate forecasting, and later matured into an epistemologist. Currently, he has a popular, long-running blog on the subjects written about here, with about 70,000 - 90,000 monthly readers.

Presents a complete argument showing why probability should be treated as a part of logic

Broadens understanding beyond frequentist and Bayesian methods, proposing a Third Way of modeling

Proposes that p-values should die, and along with them, hypothesis testing

1. Truth, Argument, Realism

1.1. Truth

1.2. Realism

1.3. Epistemology

1.4. Necessary & Conditional Truth

1.5. Science & Scientism

1.6. Faith

1.7. Belief & Knowlege

2. Logic

2.1. Language

2.2. Logic Is Not Empirical

2.3. Syllogistic Logic

2.4. Syllogisms

2.5. Informality

2.6. Fallacy

3. Induction and Intellection

3.1. Metaphysics

3.2. Types of Induction

3.3. Grue

4. What Probability Is

4.1. Probability Is Conditional

4.2. Relevance

4.3. The Proportional Syllogism

4.4. Details

4.5. Assigning Probability

4.6. Weight of Probability

4.7. Probability Usually Is Not a Number

4.8. Probability Can Be a Number

5. What Probability Is Not

5.1. Probability Is Not Physical

5.2. Probability & Essence

5.3. Probability Is Not Subjective

5.4. Probability Is Not Only Relative Frequency

5.5. Probability Is Not Always a Number Redux

6. Chance and Randomness

6.1. Randomness

6.2. Not a Cause

6.3. Experimental Design & Randomization

6.4. Nothing Is Distributed

6.5. Quantum Mechanics

6.6. Simulations

6.7. Truly Random & Information Theory

7. Causality

7.1. What Is Cause Like?

7.2. Causal Models

7.3. Paths

7.4. Once a Cause, Always a Cause

7.5. Falsifiability

7.6. Explanation

7.7. Under-Determination

8. Probability Models

8.1. Model Form

8.2. Relevance & Importance

8.3. Independence versus Irrelevance

8.4. Bayes

8.5. The Problem and Origin of Parameters

8.6. Exchangeability and Parameters

8.7. Mystery of Parameters

9. Statistical and Physical Models