Скачать презентацию Top Bar Reserved for U-M Branding and Course
Introduction.pptx
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Скачать презентацию Top Bar Reserved for U-M Branding and Course
Скачать презентацию Top Bar Reserved for U-M Branding and Course
Top Bar Reserved for U-M Branding and Course Information Critical Thinking for the Information Age Richard E. Nisbett University of Michigan
Top Bar Reserved for U-M Branding and Course Information Lesson 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
Top Bar Reserved for U-M Branding and Course Information Lesson 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
Top Bar Reserved for U-M Branding and Course Information Lesson 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
Top Bar Reserved for U-M Branding and Course Information Lesson 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
Top Bar Reserved for U-M Branding and Course Information Lesson 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
Top Bar Reserved for U-M Branding and Course Information Lesson 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
Top Bar Reserved for U-M Branding and Course Information Lesson 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 that you paid to do it
Top Bar Reserved for U-M Branding and Course Information Lesson 8: Logic and Dialectical Reasoning • Logic: Syllogisms, Conditional Reasoning • Dialectical Reasoning: Resolving Contradictory Propositions
Top Bar Reserved for U-M Branding and Course Information Text • Mindware: Tools for Smart Thinking (2015) by Richard E. Nisbett. New York: Farrar, Straus & Giroux • Available in paperback, Kindle and audio
Скачать презентацию Top Bar Reserved for U-M Branding and Course
Introduction.pptx