Cognitive Psychology and Education

Essay 1Essay 2

Making People Smarter

Chapter 11 documents the many errors people make in judgment, but the chapter also offers encouragement: We can take certain steps that improve people’s judgments. Some of those steps involve changes in the environment, so that we can, for example, ensure that the evidence people consider has been converted to frequencies (e.g., “4 cases out of 100”) rather than percentages (“4%”) or proportions (“.04”); this simple step, it seems, is enough on its own to make judgments more accurate and to increase the likelihood that people will consider base rates when drawing conclusions.

Other steps, in contrast, involve education. As the chapter mentions, training students in statistics seems to improve their ability to think about evidence—-including evidence that is obviously quantitative (e.g., a baseball player’s batting average or someone’s exam scores) and also evidence that is not, at first appearance, quantitative (e.g., thinking about how you should interpret a dancer’s audition or someone’s job interview). The benefits of statistics training are large, with some studies showing error rates in subsequent reasoning essentially cut in half.

The key element in statistical training, however, is probably not in the mathematics per se. It is valuable, for a number of purposes, to know the derivation of statistical equations or to know the procedures for using a statistics software package. For the improvement of everyday judgment, though, the key involves the new perspective that a statistics course encourages: This perspective helps you realize that certain observations (e.g., an audition or an interview) can be thought of as a sample of evidence, drawn from a larger pool of observations that potentially you could have made. The perspective also alerts you to the fact that a sample may not be representative of a broader population and that larger samples are more likely to be representative. For purposes of the statistics course itself, these are relatively simple points; but being alert to these points can have striking and widespread consequences in your thinking about issues separate from the topics and examples covered in the statistics class.

In fact, once we cast things in this way, it becomes clear that other forms of education can also have the same benefit. Many courses in psychology, for example, include coverage of methodological issues. These courses can also highlight the fact that a single observation is just a sample and that a small sample sometimes cannot be trusted. These courses sometimes cover topics that might reveal (and warn you against) confirmation bias or caution against the dangers of informally collected evidence. On this basis, it seems likely that other courses (and not just statistics classes) can actually improve your everyday thinking—and, in fact, several studies confirm this optimistic conclusion.

Ironically, though, courses in the “hard sciences”—chemistry and physics, for example—may not have these benefits. Obviously, these courses are immensely valuable for their own sake and will provide you with impressive and sophisticated skills. However, these courses may do little to improve your day-to-day reasoning. Why not? These courses plainly do involve a process of testing hypotheses through the collection of evidence, and then the quantitative analysis of the evidence. But, at the same time, let’s bear in mind that the data in, say, a chemistry course involve relatively homogeneous sets of observations: After all, the weight of one carbon atom is the same as the weight of other carbon atoms; the temperature at which water boils (at a particular altitude) is the same on Tuesday as it is on Thursday. As a result, issues of variability in the data are much less prominent in chemistry than they are, say, in psychology. (Compare how much people differ from each other to how much benzene molecules differ from each other.) This is, to be sure, a great strength for chemistry; it is one of the (many) reasons why chemistry has become such a sophisticated science. But this point means that chemists have to worry less than psychologists do about the variability within their sample, or, with that, whether their sample is of adequate size to compensate for the variability. One consequence of this is that chemistry courses often provide little practice in thinking about variability or sample size—issues that are, of course, crucial when confronting the (far messier) data provided by day to day life.

In the same way, cause-and-effect sequences are often much more straightforward in the “hard sciences” than they are in daily life: If a rock falls onto a surface, the impact depends simply on the mass of the rock and its velocity at the moment of collision. We don’t need to ask what mood the rock was in, whether the surface was expecting the rock, or whether the rock was acting peculiarly on this occasion because it knew we were watching its behavior. But these latter factors are the sort of concerns that do crop up in the “messy” sciences—and, of course, also crop up in daily life. So here, too, the hard sciences gain enormous power from the “clean” nature of their data but, by the same token, don’t provide practice in the skills of reasoning about these complications.

Which courses, therefore, should you take? Again, courses in chemistry and physics (and biology and mathematics) are important and teach you sophisticated methods and fascinating content. These courses will provide you with skills that you might not gain in any other setting. But, for purposes of improving your day-to--day reasoning, you probably want to seek out courses that involve a trio of traits: (a) the testing of hypotheses through (b) quantitative evaluation of (c) messy data. These courses will include many of the offerings of your Psychology Department, and probably some of the offerings in sociology, anthropology, political science, and economics. These, it seems, are the courses that may genuinely make you a better, more critical thinker about the conclusions you’re likely to weigh in your daily existence.

Critical Questions

1.
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Why might representing data as a frequency (e.g., "4 cases out of 100") be easier for people to understand compared to a percentage (e.g., "4%") or a proportion (e.g., ".04")?
2. How is the concept of "sampling" from a statistics class relevant to the way that we think about observations made about people and situations in daily life?
3.
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The previous two examples illustrate effects of data format and education. What are some other factors that encourage the use of System-2 reasoning?

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