Hi, I'm Richard Nisbett. I teach Psychology at the University of Michigan and this course is called Critical Thinking for the Information Age. I spent most of my career studying intelligence and reasoning, and I've learned a lot about how we reason, and how we can do it better. So, the course is intended to make you smarter. We may wonder if you can make yourself smarter. A lot of people are inclined to think that you have whatever intelligence you were intended to have, but that's not true. You've been getting smarter all your life. School has made you smarter. Your family has made you smarter. Your friends, the media and you've been making yourself smarter, because you have good rules of inference that allow you to learn about the world. As it happens, humans as a group have been getting smarter over the past few hundreds of years. This is because the Industrial Age required some new skills of people, namely the three Rs. People had to do reading, writing and arithmetic in order to hold those jobs that were coming available. For free learning those things, people improved their ability to think logically. Improve their ability to think in hypothetical terms and improve their ability to think about abstractions. The more you have of these kinds of skills, the better equipped you are to function in Industrial Age jobs. These are the kinds of thinking skills, by the way, that IQ test measure. And the more more complex the job, the more necessary those IQ type skills are. We keep on getting better, actually. It did just start a long time ago, it continues. Over the last 80 or 90 years, people and the advanced economies have gained more than 20 points in IQ which is really huge, but we live in a new era which some people call the Information Age. The IQ type skills, general intelligence aren't enough for the Information Age. You need specific tools for dealing with data, how to collect data, how to analyze the validity of data, how to find patterns in data. And equally important, how to avoid seeing patterns that aren't there. How to critic arguments based on data including, especially arguments that you encounter in the media based on poor data or poor inferences made from those data and you need also to have rules or making decision based on data collected by someone else. This is not just for professional purposes, although I'm pretty sure that most people who are watching this video will be using some of those tools in their professional life. But in the world we live in today, there are essential for being able to make sensible judgement and reasonable decisions. The needed skills here comes from statistics, probability field scientific methodology, cost-benefit analysis and cognitive psychology. I hasten to say, if you're math phobic, don't worry. If you passed arithmetic in the fourth grade, you're prepared. The most complicated math operation you will be asked to do here is division. In fact, even if you've never had a course in any of these fields, you already have a lot of the necessary skills. You've been doing statistics all your life. For example, if three friends recommend movie A and one friend recommends movie B, you're probably going to go to movie A and that's because you have an intuitive understanding of the law of large numbers. More evidence is better than less evidence, so odds are you're going to like movie A better than movie B. You've also been making probability estimates all of your life. What are the odds that the University of Michigan football team will beat the Ohio State University football team this year? What's the likelihood that you'll get a job that you've applied for? You know a lot about scientific methodology, what constitutes a good experiment, even babies do, as a matter of fact. We used to think that babies do nothing, but eat and sleep and lay around being cute, but actually they're making inferences about the world constantly using, in some cases, some pretty good rules about it from experimental science. The same is true for cost-benefit analysis. You've been making cost-benefit decisions all your life. Shall I do this or that? This involves certain costs and gets certain benefits that involves other costs and gets other benefits, but you can do cost-benefit analysis more efficiently than you do now. You can do all of these things. In fact, a lot better than you do and it isn't hard to learn. The tools are unusual, because they'll continue to get more powerful over time. Avogadro's law isn't going to stick with you, because you almost certainly don't use it. But even if you did, it would never be a more powerful tool than it was when you learned it. It's going to be used on the same things, limited things. But every time you use the law of large numbers, your ability to use it will increase. You've probably heard the expression, use a new vocabulary word twice and that's yours. In this course, it's use a new concept twice and you use it four more times in short order. This is going to be the least effort for learning you had for a while. This stuff is easy to learn. It's hard to forget and it's impossible to not use. You're going to be using these principles automatically. They're just going to push out intuitive principles, which are sub optimal for thinking. I'll give a quick overview now of the course. The first lesson is on statistics. The concepts will take up variable, normal distribution, standard deviation, correlation, reliability and validity. If you've had one course in statistics, spend a few minutes looking at this material in this lesson to see if it's helpful. I think it will be. If you've had several courses in statistics, it makes sense to go straight to lesson two and there's no math, remember. Let me say that more emphatically. There's no math, [LAUGH] so don't worry about that. Lesson two is on the law of large numbers. This states that sample values resemble population values as a function of their size. So your judgement 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 and were not very well calibrated with respect to how much error there is for various kinds of judgments. The big problem to be overcome and knowing when and how to use the law of large numbers is recognizing that there is error in having some estimate about how big it is. Lesson three is on correlation. A correlation tells us the degree of association between two variables. Correlation between mothers's height and daughters's height, between IQ and income. It's difficult to detect some correlations. And worse, we detect lots of correlations that aren't really there and there are ways to avoid that. And finally, you need protection from the media, because many reporters don't understand some of the most basic principles about correlations. Lesson four is on experiments. What makes a good experiment? Why experiments are superior to correlational evidence? The concept of natural experiments. In the 1950s, there used to be ads on TV about the town without toothache and they actually had. It was Crest, as a matter of fact who used to run those ads and he had been discovered that there were two towns very close to one another which were identical in every way about the same size, people had similar occupations, etc., but while I'm just happened to have a lot of the chemical fluoride in the water and those people have fewer toothaches. So as a consequence, Crest and almost all other toothpaste have fluoride and the breaking water and a lot of acids has fluoride. You'll learn something about how to do experiments on yourself. Does coffee make you more or less efficient, for example, or does it just make you jittery or unpleasant? And then we'll talk about the terrific costs that society pays for the experiments that it doesn't do. Lesson five is on predicting. One of the most important concepts there is the concept of regression to the mean. The basic idea here is that extreme values for any given variable are rare. And so the next value that you encounter, if you see a rare extreme variable is probably going to be less extreme. Joan is probably not going to be as extraordinarily generous the next time you see her as she was next time and you'll be learning about how to use the concept of base rate. Predictions about a case should take into account what other similar cases are like. We shouldn't just pay attention to the information we have about the particular case and question. What are other cases like? Lesson six is on cognitive biases. How we make errors in judgement, because we lack certain important concepts. One of the very oldest concepts that we'll be talking about is the Illusion of objectivity. This concept is at least 2,500 years old. We tend to think that when we see something, when we listen to something that we're understanding it in some direct, automatic way. And in fact, everything, even the simplest visual perception, we have by virtue of a lot of overlay of perceptual and cognitive processes. We'll be talking about something called the fundamental attribution error. We tend to automatically think that the behavior of objects and people is somehow produced by something internal to that object or that person abilities or traits when, in fact, often the place to look is the situation the person is in. That's the real cause of behavior. We'll be talking about heuristics, which are rules of thumb that are pretty good most of the time, but can lead as to stray when we're assessing probability and causality and we'll be talking about confirmation bias. When we test hypothesis, we tend to look only for evidence that would be supportive and not for equally valuable evidence that might be contradictory. The last lesson, seven is on choosing and deciding. We'll talk about how to carry out a cost-benefit analysis and when to ignore the results of a cost-benefit analysis, and we'll talk about the concept of opportunity costs which is how to avoid taking actions that make potentially more valuable actions impossible. And we'll talk about sunk costs, how to avoid carrying out an action for no better reason than that you paid to do it. You'll be learning that economists are a different species. They don't eat lousy food, just because they paid a lot for it. You'll be finding that, that's a useful lesson for yourself, I'm pretty sure. The last lecture before the wrap-up is on logic and dialectical reasoning. Take up two aspects of logic. One is syllogisms, all men are mortal. Socrates is a man. Therefore, Socrates is mortal, has to do with categories and quantities. Some, all, none and so on. And the other is conditional reasoning, which is a much more powerful set of reasoning skills where the conditional is if P, then Q and that's the fundamental aspect of conditional reasoning. If P is the case, then Q is the case. You know that P is the case. Therefore, you know that Q is the case. And if Q is not the case, you know that P is also not the case. The next part of that lecture has to do with what's been called dialectical reasoning and there instead of trying to find an answer to a problem which is logically compelled, you're looking to find the truth about some matter. For example, if you have two propositions that seem to be contradictory. In the logical approach, you'll want to get rid of one of those, decide which is correct. In a dialectical approach, you may be much more interested in finding out in what ways both propositions might be correct or in what way you can understand the world better by virtue of thinking simultaneously about that was two propositions. There's a text for the course called Mindware, Tools for Smart Thinking. I wrote the book. It's available in paperback, kindle and audio. Let me emphasize that you don't have to read the text in order to appreciate this course. Every lesson is intact in and of itself, and it doesn't require additional material. But if you're interested in seeing more in- depth material for the course, then you may want to look at that text. The book talks about the concepts in this course and lots of other concepts, as well. Concepts from logic, from dialectical reasoning and from philosophical analysis and philosophy of science and so on. So, each lesson will be accompanied by an assignment from that text. I hope you enjoy the course and I hope you enjoy the text, and I'll see you soon.
