Basic concepts of statistics and probability including the concepts of variable, normal distribution, standard deviation, correlation, reliability, validity, and effect size. Concrete examples are drawn from everyday life and show how the concepts can be used to solve ordinary problems.

How to think about events in such a way that they can be counted and a decision can be made about how much data is enough. You will learn about the concept of error variance and how it can be combatted by obtaining multiple observations. Your will learn that your judgments about people’s personalities are prone to serious errors that are largely avoided for judgments about abilities. And you will discover why it’s usually a mistake to interview job applicants.

It can be extremely difficult to make an accurate assessment of how two variables are related to one another; prior beliefs can be more important than data in estimating the strength of a given relationship. You will learn simple tools to estimate degree of association. You will learn about the nature of illusory correlations and how to avoid them. You will learn about the concepts of confounded variable and self-selection error.

You will learn that correlations can only rarely provide conclusive evidence about whether one variable exerts a causal influence on another and why experiments provide far better evidence about causality than correlations. You will be shown how to conduct experiments in business settings and experiments on yourself. You will learn the distinction between within subject designs and between subject designs. You will learn about the concept of artifacts and some tricks for avoiding them. You will learn how to discover natural experiments.

You will learn about the kinds of systematic errors we make when trying to predict the future. You will learn about regression to the mean and why you should assume that extreme values on a variable will be less extreme when next observed. You will learn how to think about observations in terms of true score plus error. You will learn about the concept of base rate and why it must be taken into account when estimating probabilities of specific events.

We understand the world not through direct perception but through inferential procedures that we are unaware of. Our understanding of the world is heavily influenced by schemas or abstract representations of events. We are prone to serious judgment errors that can be avoided to a degree when we understand their basis. We make guesses about probability and causality by applying the representativeness heuristic based on similarity assessments which can be very misleading. We make judgments about frequency and probability by relying in part on the availability heuristic, judging things as frequent or probable to the degree that instances come readily to mind.

How to conduct a cost-benefit analysis. Why you should throw the analysis away after doing it if the decision is personal and very important. How to avoid throwing good money after bad. How to avoid doing something that will prevent you from doing something more valuable. Why it can be expensive to try to avoid the possibility of loss. Why incentives can backfire.

The distinction between inductive logic and deductive logic. Syllogisms. Conditional reasoning. The distinction between truth of an argument and validity of an argument. The concepts of necessity and sufficiency. Venn diagrams. Common logical errors. When to avoid contradiction and when to embrace it, how to avoid undue certainty about judgments and decisions, and why attention to context rather than form is crucial for analysis of most real-world problems.