Introduction

Introduction

This first set of lectures offers some additional reasons for learning about probability, and then walks through a handful of different philosophical interpretations of the concept of probability.

This material is often not taught in formal courses on probability theory, but it is relevant to critical thinking about arguments that appeal to intuitions about chance and uncertainty.

Just to give one example, the so-called “fine-tuning” argument for the existence of God is based on the premise that we live in a "probabilistically unlikely" universe if it wasn’t the product of some kind of intelligent design (i.e. it would be unlikely to have arisen by chance), and therefore the best explanation for our existence in this universe is that it was, in fact, a product of intelligent design.

But the debate over whether this is a good argument turns (in part) on what it means for something to be “probabilistically unlikely”, and whether it’s even meaningful to talk about the universe in this way. 

I won’t say any more about that here, but this is just one example of an interesting philosophical debate where probability concepts play an important role.

(1) Video Library > 7. Reasoning About Chance and Uncertainty: Philosophy, Rules and Fallacies

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Introduction

  • Introduction

Part 1: What is Probability?: A Gentle Introduction to the Philosophy of Probability

  • Introduction

What is Probability?

  • Probability: Why Learn This Stuff?
  • What is Inductive Logic?
  • Probability as a Mathematical Object vs What That Object Represents

Interpretations of Probability

  • Classical Probability
  • Logical Probability
  • Frequency Interpretations
  • Subjective (Bayesian) Probability
  • Propensity Interpretations

Part 2: The Logic of Probability: Introduction to the Basic Rules

  • Introduction

Preliminary Concepts

  • What Has a Probability? Propositions versus Events
  • Probabilities Range Between 0 and 1
  • Mutually Exclusive Events
  • Independent Events

The Basic Rules

  • The Negation Rule: P(not-A)

Disjunction Rules: P(A or B)

  • Restricted Disjunction Rule: P(A or B) = P(A) + P(B)
  • General Disjunction Rule: P(A or B) = P(A) + P(B) - P(A and B)

Conjunction Rules: P(A and B)

  • Restricted Conjunction Rule: P(A and B) = P(A) x P(B)
  • General Conjunction Rule: P(A and B) = P(A) x P(B|A)

Conditional Rules: P(A given B)

  • Conditional Probability Rule
  • Total Probability Rule
  • Bayes’ Rule

Part 3: Probability Fallacies: Understanding the Errors We Make When Reasoning About Chance and Uncertainty

  • Introduction

Coincidences: When the Impossible Becomes Inevitable

  • Introduction
  • The Basic Fallacy
  • Borel's Law: Understanding Impossible Events
  • How to Create the Illusion of Miraculous Predictive Power
  • The Birthday Problem, Lottery Coincidences and the Power of Very Large Numbers

The Gambler's Fallacy: Bias, Randomness and the Illusion of Control

  • Introduction
  • The Basic Fallacy
  • Fairness, Bias and Independence
  • How Can You Tell Whether a Chance Setup is Unfair?
  • The Physics of Coin Tosses
  • Casino Games: Why the House Always Wins
  • Cognitive Factors and the Psychology of Gambling

Small Sample Fallacies: Looking for Causes of Extreme Cases

  • The Small Sample Fallacy: Looking for Causes of Statistical Artifacts