Mindware: Critical Thinking for the Information Age

Coursera Mindware: Critical Thinking for the Information Age

University of Michigan
3-4 hours a week
4 weeks
Paid Certificate Available
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Most professions these days require more than general intelligence. They require in addition the ability to collect, analyze and think about data. Personal life is enriched when these same skills are applied to problems in everyday life involving judgment and choice. This course presents basic concepts from statistics, probability, scientific methodology, cognitive psychology and cost-benefit theory and shows how they can be applied to everything from picking one product over another to critiquing media accounts of scientific research. Concepts are defined briefly and breezily and then applied to many examples drawn from business, the media and everyday life.

What kinds of things will you learn? Why it’s usually a mistake to interview people for a job. Why it’s highly unlikely that, if your first meal in a new restaurant is excellent, you will find the next meal to be as good. Why economists regularly walk out of movies and leave restaurant food uneaten. Why getting your picture on the cover of Sports Illustrated usually means your next season is going to be a disappointment. Why you might not have a disease even though you’ve tested positive for it. Why you’re never going to know how coffee affects you unless you conduct an experiment in which you flip a coin to determine whether you will have coffee on a given day. Why it might be a mistake to use an office in a building you own as opposed to having your office in someone else’s building. Why you should never keep a stock that’s going down in hopes that it will go back up and prevent you from losing any of your initial investment. Why it is that a great deal of health information presented in the media is misinformation.

Individuals and cultures can make themselves smarter. Since the beginning of the Industrial Revolution, people have become enormously smarter. The Information Age requires a brand-new set of skills involving statistics, probability, cost-benefit analysis, principles of cognitive psychology, logic and dialectical reasoning.

Lesson 1: Statistics
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.

Lesson 2: The Law of Large Numbers
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.

Lesson 3: Correlation
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.

Lesson 4: Experiments
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.

Lesson 5: Prediction
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.

Lesson 6: Cognitive Biases
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.

Lesson 7: Choosing and Deciding
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.

Lesson 8: Logic and Dialectical Reasoning
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.


Taught by
Richard E. Nisbett
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