Who said the UN doesn’t do anything useful? Courtesy of that august body, it’s World Statistics Day!
Official statistics help decision makers develop informed policies that impact millions of people. Improved data sources, sound statistical methods, new technologies and strengthened statistical systems enable better decisions that eventually result in better lives for all of us. On 20 October 2015, the global statistical community will showcase their achievements and their ongoing work to help this vision come true. [my emphasis]
Amen to sound statistical methods, and may unsound methods be consigned to the flames (as Hume might say).
Book update. I received an email from a colleague, much more well known than I, who said Wiley asked him to read/review my book. He’s on the outs with Wiley and said no (they weren’t offering any moola, either). But I still took it as good news that the thing is being circulated. Springer also is reviewing.
To have a small—only a small—flavor of the book, these classic posts give some idea. Though the book, rejoicing (thus far) under the title The Philosophy of Uncertainty: An Introduction, is much more than these scanty posts and papers.
- Unsignificant Statistics: Or Die P-Value Die Die Die;
- Hypothesis Testing Relies On The Fallacy Of False Dichotomy;
- The Third Way Of Probability & Statistics: Beyond Testing and Estimation To Importance, Relevance, and Skill
- The Crisis Of Evidence: Why Probability And Statistics Cannot Discover Cause
- The Problem Of Grue Isn’t
- On Probability Leakage
- It is Time to Stop Teaching Frequentism to Non-statisticians
- On the non-arbitrary assignment of equi-probable priors
Our friend Mademoiselle Deborah Mayo has the right idea with this title: “Statistical ‘reforms’ without philosophy are blind” We disagree about p-values, causation, hypothesis testing, the importance of parameters (I say they aren’t), and the nature of probability. But besides that, we’re sympatico that philosophy needs to be involved.
Here’s another false dichotomy for you: if you’re not a frequentist, you must be a Bayesian. I have much sympathy with the latter approach, but Bayesian Statistics Isn’t What You Think.
What’s that you ask? You’d like to see the Table of Contents of the book? Well, okay. Here:
1 Truth, Argument, Realism
1.4 Necessary \& Conditional Truth
1.5 Science \& Scientism
1.7 Belief \& Knowledge
2.2 Logic is Not Empirical
2.3 Syllogistic Logic
3 Induction \& Intellection
3.2 Types of Induction
4 What Probability Is
4.1 Probability Is Conditional
4.3 The Proportional Syllogism
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 \& Randomness
6.2 Not a Cause
6.3 Experimental Design \& Randomization
6.4 Nothing is Distributed
6.5 Quantum Mechanics
6.7 Truly Random \& Information Theory
7.1 What is Cause Like?
7.2 Causal \& Deterministic Models
7.4 Once A Cause, Always A Cause
8 Probability Models
8.1 Model Form
8.2 Relevance \& Importance
8.3 Independence Versus Irrelevance
8.5 The Problem And Origin Of Parameters
8.6 Exchangeability And Parameters
8.7 Mystery Of Parameters
9 Statistical \& Physical Models
9.1 The Idea
9.2 The Best Model
9.3 Second-Best Models
9.4 Relevance And Importance
9.6 Hypothesis Testing
9.7 Die, P-value, Die, Die, Die
9.8 Implementing Statistical Models
9.9 Model Goodness
10 Modelling Goals, Strategies, \& Mistakes
10.3 Epidemiologist Fallacy
10.4 Quantifying The Unquantifiable
10.5 Time Series
10.6 The Future
Comes to about 300 pages in a trade paperback.
Don’t forget I’m ready and rarin’ to go to lecture on these topics. Time is finite, so get your orders in early for the pieces of it I have to offer.