Some observations on cognitive bias drawn from the book How we know what isn't so by Thomas Gilovich.
We have many beliefs that arent true. Examples:
Why do they believe these things if they arent true?
Today, more people believe in ESP than in evolution. In the US, there are 20 times more astrologers than astronomers.
It is not simply lack of exposure to evidence: they hold the beliefs in spite of evidence. Nor is it stupidity. Probably the opposite: evolution has given us brains that can process huge amounts of information using a variety of simple cognitive and perceptual processes. These processes are a great strength, but they are also the cause of some of our biggest follies.
Some of these cognitive principles:
Some of the motivational and social determinants of false beliefs:
Why do we care about erroneous beliefs? They are the reason behind some of humanitys most egregious and senseless acts, such as
Misperception of Random Data
People often see patterns that are not really there.
The ability to spot real patterns is the key to human success. We can exploit regularities we observe in nature to build technology. So the tendency to see pattern is evolutionarily adaptive.
A folk theory: in basketball, success leads to success. Getting a basket gives you confidence, and this helps you get the next basket. And so on. The result is hot streaks. Virtually everyone believes this, including coaches and the players themselves. But the data contradict this. (see page 13 of Gilovich).
Of course, it could be that some other process is masking the effects of "hot hand". Like that a person who is hot gets extra coverage by the defense. But even if you examine controlled situations like free throws, you see that probability of getting a basket is the same regardless of the success or failure of the previous shot.
Or it could be that the essence of hot hand is not success but predictability: they know whether the next shot will hit or not. So this was tested experimentally. But players predictions were not correlated with the outcomes of their shots at all.
So why do we believe in hot streaks when they dont really happen? One reason is that people have faulty images of what chance sequences look like. People expect that a coin tossed many times will more or less alternate heads and tails. If there are sequences of 4 or 5 heads in a row, they think there is something non-random going on. But in fact those are quite common. For example, a sequence like OXXXOXXXOXXOOOXOOXXOO looks non-random, but it is.
It looks non-random because there are so many clusters, and we dont expect clusters in random data. Why not? Because of the representativeness axiom that people seem to use to think with. We evaluate whether an outcome is likely or not based on similar on a few simple features to what we would expect given the cause. For example, we will believe that someone is a librarian if they look bookish they are representative of the category. The salient feature of independent events like coin tosses is that in the long run, we expect the two outcomes to occur an equal number of times: 50-50. But this is in the long run. Yet we expect the same result in the short run as well, so if a given sequence has 9 heads and 1 tail, we think there is something wrong with it.
There are lots of phenomena that are like this:
Both areas are filled with folk theories governing the outcomes. In births, there are theories involving the phases of the moon. In stocks, there are dozens of strange theories, like the hemline theory and the Super Bowl theory.
Even statistical analysis does not always help dispel the illusion. With hindsight, we can always pick the most unusual features of the data and build an analysis around them. Example is the shown in pg 20 pattern of bombs dropped on London. By choosing the right quadrants, we can make it look non-random.
After-the-Fact, Ad Hoc Explanations
Easy to create a story that justifies an outcome. Experiments with split brains show this easily. The right brain is made to choose something based on something presented to the right brain only. The left brain is then asked why they chose that. There is never any hesitation: they make it up instantly.
Whenever two variables are imperfectly correlated, extreme values of one variable are always matched by less extreme values of the other variable. We have trouble internalizing this. So in life, we tend to assume that extraordinary performance in one year will be matched the extraordinary performance the next year, but this is rarely the case. This affects how we buy mutual funds and other stocks, how we hire people, lend money to businesses, etc.
If I tell you that someone who is in 90th percentile of sense of humor, you tend to predict that their gpa will be in the 90th percentile. Yet, if the correlation between sense of humor and gpa is near zero, your best guess for their gpa would 50th percentile.
Instead of recognizing regression effects, we tend to interpret it. If someone who score very high before scores less high now, we think they got overconfident, or slacked off or were resting on laurels etc.
This may be why most people, like parents, use punishment more often than reward, even though psychological research suggests that reward works better. We give rewards when someone has done something extraordinarily well. Then, of course, they dont do as well the next time, so you think the reward was not effective. In contrast, we give punishments when someone really screws up. And of course, the next time, they dont screw up as much, so we think the punishment was effective. But it was just the regression effect: the lack of correlation between the events.
An experiment of this kind was done by having a teacher deal with a students lateness. A computer showed the teacher what time the kid arrived each day, and each day the teacher could either issue praise, punishment, or no comment. After several "days" of this, the teacher was asked which seemed to be most effective, punishment or reward. Most felt that punishment was more effective. What they didnt know was that the students arrival time was pre-programmed and completely unrelated to the what the teacher did.
Can combine the clustering illusion with the regression fallacy: seeing streaks or clumps of events, and then having their subsequent absence be interpreted causally. Example is the trip to Israel described on pg 28. A flurry of deaths due to natural causes in the northern part of Israel led to speculation of some new threat. A group of rabbis attributed the problem to the sacrilege of allowing women to attend funerals, which was previously forbidden. So they decreed that women could not attend funerals anymore. Soon, the rash of deaths subsided, and this was taken as confirmation that the remedy was effective!
In other words, simple features of human cognition can account for major beliefs such as the proper role of women in society.
Incomplete and Unrepresentative Data
"I know someone who cured themselves of cancer through positive thinking." "Of course there is a sophomore slump you see it all the time."
If something is true à then there should be some evidence of it. But the existence of some instances do not prove the general case. If mixing a package of pop rocks with coke will kill you, then there should be some cases of people who did that and died immediately after. But just because somebody eats pop rocks with coke and dies immediately after doesnt meant that there is any connection between the two!
We tend to believe in things because we saw it happen once. We also tend to give too much weight to evidence that supports the belief, while ignoring evidence that disconfirms it.
Take the claim that "african-americans like volvos". If you believe that, you tend to find supported evidence everywhere: every time you see an african american driving a volvo you think "see, I told you!". But you dont really notice volvos driven by whites, or other cars (not volvos) driven by african-americans.
|African American||a. you notice these the most!||b. These are also seen|
|Other person||c. These you ignore ignore as irrelevant||d. You may notice these|
Yet all the cells are needed to evaluate the claim. Compare this table:
With this one:
If you focus only on the (a) and (b) cells, the two situations look the same. But only the first table shows an actual relationship between Race and Car Manufacturer. The second table provides clear evidence that there is NO relationship between Race and Car Manufacturer. (Just convert the data to percentages and youll see.)
Similarly, if you are trying to evaluate whether seeding clouds is effective in making rain, you tend to look at only the (a) cases. These are unambiguously relevant. But the (c) cases tend to be ignored because they are ambiguous: they dont clearly speak to the issue.
|Seed clouds||(a) you are persuaded by
many cases of this type
|Dont seed||(c) ambiguous ignored|
It is particularly difficult to deal with variables in which one category signifies the absence of the other, as in "rains" and "not rains". We have difficulty working with the "not rains" category. In contrast, we do better with "Male" and "female".
We generally favor the positive. John Holt played 20 questions with kids. He would say, Im thinking of a number between 1 and 10,000. You have twenty yes/no questions to guess the answer. So someone would say is the number less than 5,000? and if the answer was Yes, the kids would cheer. If it was NO, they would groan. But its just as useful no matter how it comes out!
Suppose your job is to figure out what questions to ask a respondent to determine whether they are an extrovert. Most people ask things like "what would you do to liven up party?" They are thinking about behaviors that extroverts do. They dont think to ask "do you like to curl up in front of a fire and read a novel", even though a yes answer would help to determine that the person is not an extrovert.
If asked to determine whether the respondent is an introvert, they ask about introverted behaviors.
Suppose we evaluate whether students with high sat scores really do perform better in college. We can look at the sat scores of good and bad students in colleges, but notice an important point: students with really bad sat scores dont get into college at all. So you really cant examine disconfirming cases, like students with bad sat scores that do well in college: they are artificially removed from the game. Similarly, studnets with really bad gpas, who might have had good sat scores (contrary to hypothesis), are bounced out and again are not available in your sample. Also, students with different sat scores dont go to the same schools, so it is hard to assess the effect of sat on gpa alone.