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Critical Thinking for the Information Age
1. Critical Thinking for the Information Age
Top Bar Reserved for U-M Branding and Course InformationCritical Thinking for the
Information Age
Richard E. Nisbett
University of Michigan
2. Lesson 1: Statistics
Top Bar Reserved for U-M Branding and Course InformationLesson 1: Statistics
• Concepts: variable, normal distribution, standard
deviation, correlation, reliability and validity
• If you’ve had one course spend a few minutes looking at
material to see if it’s helpful
• If you’ve had several courses in statistics it makes sense
to go straight to Lesson 2
• No math
• No math
3. Lesson 2: Law of Large Numbers
Top Bar Reserved for U-M Branding and Course InformationLesson 2: Law of Large Numbers
• Sample values resemble population values as a
function of their size
– E.g., your judgment about Bill’s honesty or the quality of food in the new restaurant
becomes more accurate the more evidence you have
• This is especially true when there’s a lot of error in
your sample
• The big problem to be overcome: recognizing that
there is error
4. Lesson 3: Correlation
Top Bar Reserved for U-M Branding and Course InformationLesson 3: Correlation
• A correlation tells us the degree of association
between two variables
• E.g., mothers’ height and daughters’ height, IQ and
income
• Difficult to detect some correlations
• Worse: we detect lots of correlations that aren’t really
there
• You need protection from the media – because many
reporters don’t understand these principles
5. Lesson 4: Experiments
Top Bar Reserved for U-M Branding and Course InformationLesson 4: Experiments
• What makes a good experiment
• Why experiments are superior to correlational
evidence
• The concept of natural experiments:
– E.g., the town without toothache
• How to do experiments on yourself
– E.g., does coffee make you more or less efficient?
• The terrific costs society pays for the
experiments it doesn’t do
6. Lesson 5: Predicting
Top Bar Reserved for U-M Branding and Course InformationLesson 5: Predicting
• Concept of regression to the mean
– Extreme values are rare, the next value you encounter is probably
going to be less extreme
– E.g., Joan is probably not going to be as extraordinarily generous
the next time you see her
• Concept of base rate
– Predictions about a case should take into account what other
similar cases are like
7. Lesson 6: Cognitive Biases
Top Bar Reserved for U-M Branding and Course InformationLesson 6: Cognitive Biases
• How we make errors in judgment because we lack some
important concepts
• Illusion of Objectivity
• Fundamental Attribution Error
• Heuristics: rules of thumb that can lead us astray when
assessing probability and causality
• Confirmation bias: when testing hypotheses we tend to look
only for evidence that would be supportive, not for equally
valuable evidence that might be contradictory
8. Lesson 7: Choosing and Deciding
Top Bar Reserved for U-M Branding and Course InformationLesson 7: Choosing and Deciding
• How to carry out a cost/benefit analysis
• Opportunity Costs
– How to avoid taking actions that make potentially more valuable
actions impossible
• Sunk Costs
– How to avoid carrying out an action for no better reason than that
you paid to do it
9. Lesson 8: Logic and Dialectical Reasoning
Top Bar Reserved for U-M Branding and Course InformationLesson 8: Logic and Dialectical
Reasoning
• Logic: Syllogisms, Conditional Reasoning
• Dialectical Reasoning: Resolving Contradictory
Propositions
10. Text
Top Bar Reserved for U-M Branding and Course InformationText
• Mindware: Tools for Smart Thinking (2015) by Richard E.
Nisbett. New York: Farrar, Straus & Giroux
• Available in paperback, Kindle and audio