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.
- Week 1 - Introduction
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, prin...
- Week 1 - 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 ...
- Week 1 - 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 y...
- Week 2 - 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 as...
- Week 2 - 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 exp...
- Week 3 - 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 t...
- Week 3 - 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...
- Week 4 - 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 mo...
- Week 4 - 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 a...
- Week 4 - Conclusion
Richard E. Nisbett
Theodore M. Newcomb Distinguished University Professor
Department of Psychology
L'Université du Michigan (UM, UMich ou simplement Michigan) est une université publique de recherche située à Ann Arbor, dans le Michigan, aux États-Unis. Fondée en 1817, l'université est la plus ancienne et la plus grande du Michigan.
La mission de l'université du Michigan est de servir les habitants du Michigan et le monde entier en occupant une place prépondérante dans la création, la communication, la préservation et l'application des connaissances, de l'art et des valeurs académiques, et en formant des dirigeants et des citoyens qui défieront le présent et enrichiront l'avenir.
Coursera - это цифровая компания, предлагающая массовые открытые онлайн-курсы, основанные учителями компьютеров Эндрю Нгом и Стэнфордским университетом Дафни Коллер, расположенные в Маунтин-Вью, штат Калифорния.
Coursera работает с ведущими университетами и организациями, чтобы сделать некоторые из своих курсов доступными в Интернете, и предлагает курсы по многим предметам, включая: физику, инженерию, гуманитарные науки, медицину, биологию, социальные науки, математику, бизнес, информатику, цифровой маркетинг, науку о данных и другие предметы.