An Index of Cognitive Biases

Here's an index to all the installments of Cognitive Biases. Click on any "Part" name to go directly to that installment. You can also find the bias you’re interested in by clicking in the tag cloud on the right. To find all posts concerning cognitive biases, click the very big phrase.

Part 1 — Bias blind spot, confirmation bias, déformation professionnelle, denomination effect, moral credential effect.

Part 2 — Base rate fallacy, congruence bias, experimenter’s bias

Part 3 — Ambiguity aversion effect (Ellsberg paradox), choice-supportive bias, distinction bias, contrast effect

Part 4 — Actor-observer bias, anchoring effect, attentional bias, availability cascade, belief bias

Part 5 — Clustering illusion, conjunction fallacy, cryptomnesia

Part 6 — Disposition effect, egocentric bias, endowment effect, extraordinarity bias

Part 7 — False consensus effect, false memory, Forer effect, framing, fundamental attribution error

Part 8 — Gambler’s fallacy, halo effect

Part 9 — Hawthorne effect, herd instinct, hindsight bias, hyperbolic discounting

Part 10 — Illusion of asymmetric insight, illusion of control, illusory superiority, impact bias, information bias, ingroup bias, irrational escalation

Part 11 — Just-world phenomenon, loss aversion, ludic fallacy, mere exposure effect, money illusion

Part 12 — Need for closure, neglect of probability, “not-invented-here” (NIH) syndrome, notational bias

Part 13 — Observer-expectancy effect, omission bias, optimism bias, ostrich effect, outgroup homogeneity bias, overconfidence effect

Part 14 — Pareidolia, planning fallacy, post-purchase rationalization

Part 15 — Projection bias, pseudocertainty effect, publication bias

Part 16 — Reactance, reminiscence bump, restraint bias, rosy retrospection

Part 17 — Selection bias, selective perception, self-fulfilling prophecy

Part 18 — Self-serving bias, Semmelweis reflex, serial position effect

Part 19 — Status quo bias, stereotyping, subadditivity effect

Part 20 — Subjective validation, suggestibility, system justification theory

Part 21 — Telescoping effect, Texas sharpshooter fallacy, trait ascription bias

Part 22 — Ultimate attribution error, valence effect, von Restorff effect, wishful thinking, zero-risk bias

A Credit To His Race (The final installment of Cognitive Biases)

At long last, we reach the end of our series on Cognitive Biases. In this installment, we'll study the ultimate attribution error, the valence effect, the von Restorff effect, wishful thinking, and the zero-risk bias.


Ultimate Attribution Error

A phrase I used to hear from time to time in my Alabama days was, “He’s a credit to his race.” It was never used to refer to a white person, of course, but only to blacks. On the surface, it appears to be a compliment, but it’s an example of the ultimate attribution error.

In the ultimate attribution error, people view negative behaviors on the part of members of an outgroup as a normal trait, and positive behavior as exceptions to the norm. It relates to the fundamental attribution error, in which we explain our own behavior as reactions to situations and other peoples’ behavior as a matter of basic character, and clearly relates to stereotyping. Ultimate attribution error is one of the basic mechanisms of prejudice.

Valence Effect

In psychology, valence refers to the positive or negative emotional charge of a given event or circumstance. The valence effect is a probability bias in which people overestimate the likelihood of something good rather than something bad: it’s the basic mechanism that stimulates the sale of lottery tickets.

There are numerous studies that demonstrate the valence effect. In one study, people assigned a higher probability of picking a card with a smiling face than one with a frowning face in a random deck.

The valence effect can be considered as wishful thinking, but it’s been shown in some case that belief in a positive outcome can increase the odds of achieving it — you may work harder or refuse to give up as early.

Von Restorff Effect

First identified by Dr. Hedwig von Restorff in 1933, this bias (also called the isolation effect) predicts that an item that "stands out like a sore thumb" (called distinctive encoding) is more likely to be remembered than other items. For instance, if a person examines a shopping list with one item highlighted in bright green, he or she will be more likely to remember the highlighted item than any of the others.

Wishful Thinking

This popular cognitive bias involves forming beliefs and making decisions based on your imagination rather than evidence, rationality, or reality. All else being equal, the valence effect holds: people predict positive outcomes are more likely than negative ones.

There is also reverse wishful thinking, in which someone assumes that because it’s bad it’s more likely to happen: Murphy’s Law as cognitive bias.

Wishful thinking isn’t just a cognitive bias, but a logical fallacy: I wish that P would be true/false; therefore, P is true/false. It’s related to two other fallacies that are reciprocals of one another: negative proof and argument from ignorance.

In negative proof, the absence of certainty on one end of the argument is taken as proof of the opposite end: climate scientists cannot say with 100% certainty that their claims about global warming are true, therefore, they must be false. The reciprocal fallacy is known as the argument from ignorance: no one can be sure that there is no God; therefore, there is a God.

Zero-Risk Bias

Since 2000, terrorists attacks against the United States or Americans abroad have killed about 3,250 people, the vast majority of them on 9/11. Your odds of being a victim are about one in ten million.

The Transportation Security Administration consumes $5.6 billion a year. Its job is to reduce the chance of terrorist attacks on transportation infrastructure, primarily air, to zero. Let’s assume that they are completely effective in their mission. If so, the cost per life saved is $1.7 million.

Perhaps that’s a completely reasonable price to pay to save a human life. However, from a logical point of view, you have to consider what else $5.6 billion might accomplish. Over a ten-year period, about 420,000 people die in car accidents. If $5.6 billion would eliminate 100% of the risk of aviation terrorist deaths, or 10% of the risk of car accident deaths, which risk would you chose to attack?

Common sense argues for a 10% reduction in car accidents, but the zero-risk bias argues the opposite: it’s the preference for completely eliminating a risk (even if small) to reducing a larger risk. It values certainty over residual risk.

There are other arguments that can be made in support of anti-terrorist activities, but the zero-risk bias is also operational here, and it leads to faulty decisions.


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With this installment, our long march through the wilds of cognitive bias comes to an end. I deeply appreciate the many insightful comments you’ve provided.


And now for something completely different…

Did You Hear the One About the Texas Sharpshooter? (Part 21 of Cognitive Biases)

In this installment of Cognitive Biases, we'll learn why your memories get unstuck in time, why establishing hypotheses backward is a fallacy, and why we think other people always behave the same way.


Telescoping Effect

The telescoping effect is a memory bias, first documented in a 1964 article in the Journal of the American Statistical Association. People tend to perceive recent events as being more remote in time than they are (backward telescoping) and more remote events as being more recent than they are. The Galton-Crovitz test measures the effect; you can take the test here.

Texas Sharpshooter Fallacy

A sales manager I once knew had an infallible sense of what was going to sell. Because he didn’t want to waste his time, he put all his emphasis on selling what he knew would sell, and didn’t bother pushing the stuff that wouldn’t sell anyway.

This is an example of the Texas sharpshooter fallacy. The Texas sharpshooter, you see, fired a bunch of shots at the side of the barn, went over and found a cluster of hits, and drew a bullseye over them. When you don’t establish your hypothesis first and test it second, your conclusion is suspect.

This was first described in the field of epidemiology. For example, the number of cases of disease D in city C is greater than would be expected by chance. City C has a factory that has released amounts of chemical agent A into the environment. Therefore, agent A causes disease D.

Not so fast.

The cluster may be the result of chance, or there may be another cause. Now, if you conclude that agent A should be tested as a possible trigger of disease D, that’s a reasonable inference.

Finding a Nostradamus prophecy that could arguably relate to a big event in history is another example. Here’s a famous prophecy that appears to predict Hitler:

Beasts wild with hunger will cross the rivers,

The greater part of the battle will be against Hister.

He will cause great men to be dragged in a cage of iron,
When the son of Germany obeys no law.

But out of a thousand prophecies, what are the odds that none of them will relate to a real event?


Trait Ascription Bias

Trait ascription bias is the tendency for people to view themselves as relatively variable in terms of personality, behavior and mood while viewing others as much more predictable in their personal traits across different situations. This may be because our own internal states are much more observable and available to us than those of others. A similar bias on the group level is called the outgroup homogeneity bias.

The degree to which we fall into this bias often depends on how well we know the other person, but not entirely. “You always behave like that” is an accusation most of us have leveled at a loved one at some time in our lives.


More next week.




Previous Installments

You can find the bias you’re interested in by clicking in the tag cloud on the right. To find all posts concerning cognitive biases, click the very big phrase.

Part 1 — Bias blind spot, confirmation bias, déformation professionnelle, denomination effect, moral credential effect.

Part 2 — Base rate fallacy, congruence bias, experimenter’s bias

Part 3 — Ambiguity aversion effect (Ellsberg paradox), choice-supportive bias, distinction bias, contrast effect

Part 4 — Actor-observer bias, anchoring effect, attentional bias, availability cascade, belief bias

Part 5 — Clustering illusion, conjunction fallacy, cryptomnesia

Part 6 — Disposition effect, egocentric bias, endowment effect, extraordinarity bias

Part 7 — False consensus effect, false memory, Forer effect, framing, fundamental attribution error

Part 8 — Gambler’s fallacy, halo effect

Part 9 — Hawthorne effect, herd instinct, hindsight bias, hyperbolic discounting

Part 10 — Illusion of asymmetric insight, illusion of control, illusory superiority, impact bias, information bias, ingroup bias, irrational escalation

Part 11 — Just-world phenomenon, loss aversion, ludic fallacy, mere exposure effect, money illusion

Part 12 — Need for closure, neglect of probability, “not-invented-here” (NIH) syndrome, notational bias

Part 13 — Observer-expectancy effect, omission bias, optimism bias, ostrich effect, outgroup homogeneity bias, overconfidence effect

Part 14 — Pareidolia, planning fallacy, post-purchase rationalization

Part 15 — Projection bias, pseudocertainty effect, publication bias

Part 16 — Reactance, reminiscence bump, restraint bias, rosy retrospection

Part 17 — Selection bias, selective perception, self-fulfilling prophecy

Part 18 — Self-serving bias, Semmelweis reflex, serial position effect

Part 19 — Status quo bias, stereotyping, subadditivity effect

Part 20 — Subjective validation, suggestibility, system justification theory

That Psychic Was So *Accurate*! (Part 20 of Cognitive Biases)

In our 20th installment of Cognitive Biases, we cover subjective validation, the tendency to think a statement is true if it means something to us; suggestibility, the extent to which we accept or act on the suggestions of others; and system justification theory, the cognitive bias of patriotism.


Subjective Validation

Subjective validation, also known as the personal validation effect, is the tendency to consider a statement correct if it’s meaningful to the listener. It’s related to the Forer effect and validated by confirmation bias, and it’s the basic technique that reinforces belief in paranormal phenomena. The listener focuses on and remembers the accurate statements and forgets or ignores the inaccurate ones, forming an impression of the psychic’s success that is wildly inflated.

Say anything, and it’s possible to find meaning in it. “I sense a father figure trying to contact you from the spirit world,” becomes validated if there’s anyone in the subject’s life that can be made to qualify. “I hear the phrase ‘broken wheel,’” the psychic says, and of all the thousands of possible associations, the subject finds one with personal meaning, and the psychic is validated.

What if the phrase ‘broken wheel’ evokes no associations? Then the psychic says, “I hear the name ‘Charles,’” and so forth until there’s a winner. Selective memory comes into play as well, so the subject doesn’t remember the ‘broken wheel’ figure, but remembers the ‘Charles’ association vividly.

The strength of the effect depends less on the skill of the psychic, of course, and much more on the level of desire of the subject. If we want to believe, we’ll find the evidence we need.

Suggestibility

You are suggestible to the extent you are inclined to accept or act on the suggestions of others. Some people are naturally more suggestible than others, of course, but suggestibility in individuals is varied. Intense emotions, current level of self-esteem or assertiveness, and age play a role.
The nature of suggestibility plays a big role in hypnosis. There are three different types of suggestibility, according to Dr. John Kappas.

• Emotional Suggestibility. A suggestible behavior characterized by a high degree of responsiveness to inferred suggestions that affect emotions and restrict physical body responses; usually associated with hypnoidal depth. Thus the emotional suggestible learns more by inference than by direct, literal suggestions.
• Physical Suggestibility. A suggestible behavior characterized by a high degree of responsiveness to literal suggestions affecting the body, and restriction of emotional responses; usually associated with cataleptic stages or deeper.
• Intellectual Suggestibility. The type of hypnotic suggestibility in which a subject fears being controlled by the operator and is constantly trying to analyze, reject or rationalize everything the operator says. With this type of subject the operator must give logical explanations for every suggestion and must allow the subject to feel that he is doing the hypnotizing himself.

With all of that, there’s surprisingly little consensus on what suggestibility is and how it works. Is it a function of character, a learned habit, a function of language acquisition and empathy, a biased term used to provoke people to greater resistance, or something else?

Common examples of suggestible behavior in everyday life include "contagious yawning" (multiple people begin to yawn after observing a person yawning) and the medical student syndrome (a person begins to experience symptoms of an illness after reading or hearing about it).

Placebo response may also be based on individual differences in suggestibility, at least in part. Suggestible persons may be more responsive to various forms of alternative health practices that seem to rely upon patient belief in the intervention. People who are highly suggestible may be prone to making poor judgments because they did not process suggestions critically and falling pray to emotion-based advertising.


System Justification Theory

System justification theory (SJT) is a scientific theory within social psychology that proposes people have a motivation to defend and bolster the status quo, that is, to see it as good, legitimate, and desirable.

According to system justification theory, people not only want to hold favorable attitudes about themselves (ego-justification) and their own groups (group-justification), but they also want to hold favorable attitudes about the overarching social order (system-justification). A consequence of this tendency is that existing social, economic, and political arrangements tend to be preferred, and alternatives to the status quo are disparaged.

Early SJT research focused on compensatory stereotypes. Experiments suggested that the widespread endorsement of stereotypes such as "poor but happy" or "rich but miserable" exist to balance out the gap between those of low and high socioeconomic status.,Later work suggested that these compensatory stereotypes are preferred by those on the left while people on the right prefer non-complimentary stereotypes such as "poor and dishonest" or "rich and honest", which rationalize inequality rather than compensate for it.

According to system justification theory, this motive is not unique to members of dominant groups, who benefit the most from the current regime; it also affects the thoughts and behaviors of members of groups who are seemingly incurring disadvantages by it (e.g., poor people, racial/ethnic minorities). System justification theory therefore accounts for counter-intuitive evidence that members of disadvantaged groups often support the societal status quo (at least to some degree), often at considerable cost to themselves and to fellow group members.

System justification theory differs from the status quo bias in that it is predominately motivational rather than cognitive. Generally, the status quo bias refers to a tendency to prefer the default or established option when making choices. In contrast, system justification posits that people need and want to see prevailing social systems as fair and just. The motivational component of system justification means that its effects are exacerbated when people are under psychological threat or when they feel their outcomes are especially dependent on the system that is being justified.

More next week.

To read the whole series, click "Cognitive bias" in the tag cloud to your right, or search for any individual bias the same way.

When 1+1=3 (Part 19 of Cognitive Biases)

Our 19th installment of Cognitive Biases covers the status quo bias, stereotyping, and the subadditivity effect.


Status Quo Bias

Sigmund Freud suggested that there were only two reasons people changed: pain and pressure. Evidence for the status quo bias, a preference not to change established behavior (even if negative) unless the incentive to change is overwhelming, comes from many fields, including political science and economics.

Another way to look at the status quo bias is inertia: the tendency of objects at rest to remain at rest until acted upon by an outside force. The corollary, that objects once in motion tend to stay in motion until acted upon by an outside force, gives hope for change. Unfortunately, one of those outside forces is friction, which is as easy to see in human affairs as it is in the rest of the material universe.

Daniel Kahneman (this time without Amos Tversky) has created experiments that can produce status quo bias effects reliably. It seems to be a combination of loss aversion and the endowment effect, both described elsewhere.

The status quo bias should be distinguished from a rational preference for the status quo in any particular incident. Change is not in itself always good.

Stereotyping

A stereotype, strictly speaking, is a commonly held popular belief about a specific social group or type of individual. It’s not identical to prejudice:

• Prejudices are abstract-general preconceptions or abstract-general attitudes towards any type of situation, object, or person.
• Stereotypes are generalizations of existing characteristics that reduce complexity.

The word stereotype originally comes from printing: a duplicate impression of an original typographic element used for printing instead of the original. (A cliché, interestingly, is the technical term for the printing surface of a stereotype.) It was journalist Walter Lippmann who first used the word in its modern interpersonal sense. A stereotype is a “picture in our heads,“ he wrote, “whether right or wrong.“

Mental categorizing and labeling is both necessary and inescapable. Automatic stereotyping is natural; the necessary (but often omitted) follow-up is to make a conscious check to adjust the impression.

A number of theories have been derived from sociological studies of stereotyping and prejudicial thinking. In early studies it was believed that stereotypes were only used by rigid, repressed, and authoritarian people. Sociologists concluded that this was a result of conflict, poor parenting, and inadequate mental and emotional development. This idea has been overturned; more recent studies have concluded that stereotypes are commonplace.

One theory as to why people stereotype is that it is too difficult to take in all of the complexities of other people as individuals. Even though stereotyping is inexact, it is an efficient way to mentally organize large blocks of information. Categorization is an essential human capability because it enables us to simplify, predict, and organize our world. Once one has sorted and organized everyone into tidy categories, there is a human tendency to avoid processing new or unexpected information about each individual. Assigning general group characteristics to members of that group saves time and satisfies the need to predict the social world in a general sense.

Another theory is that people stereotype because of the need to feel good about oneself. Stereotypes protect one from anxiety and enhance self-esteem. By designating one's own group as the standard or normal group and assigning others to groups considered inferior or abnormal, it provides one with a sense of worth, and in that sense, stereotyping is related to the ingroup bias.

Subadditivity Effect

The subadditivity effect is the tendency to judge probability of the whole to be less than the probabilities of the parts.

For instance, subjects in one experiment judged the probability of death from cancer in the United States was 18%, the probability from heart attack was 22%, and the probability of death from "other natural causes" was 33%. Other participants judged the probability of death from a natural cause was 58%. Natural causes are made up of precisely cancer, heart attack, and "other natural causes," however, the sum of the latter three probabilities is 73%. According to Tversky and Koehler in a 1994 study, this kind of result is observed consistently.

The subadditivity effect is related to other math-oriented cognitive biases, including the denomination effect, the base rate fallacy, and especially the conjunction fallacy.

More next week.

To read the whole series, click "Cognitive bias" in the tag cloud to your right, or search for any individual bias the same way.

Rashomon Reality (Part 18 of Cognitive Biases)

Our 18th installment of Cognitive Biases covers the self-serving bias, offers a new interpretation of the Semmelweis reflex, and looks at the two sides of the serial position effect.

Self-Serving Bias

A self-serving bias occurs when people attribute their successes to internal or personal factors but attribute their failures to situational factors beyond their control: to take credit for success but to shift the blame for failure. It also occurs when we are presented with ambiguous information and evaluate it in the way that best suits our own interest.

Several reasons have been proposed to explain the occurrence of self-serving bias: maintaining self-esteem, making a good impression, or sometimes that we’re aware of factors outsiders might miss.

The bias has been demonstrated in many areas. For example, victims of serious occupational accidents tend to attribute their accidents to external factors, whereas their coworkers and management tend to attribute the accidents to the victims' own actions.

When the self-serving bias causes people to see Rashomon reality, the ability to negotiate can be dramatically impaired. One of the parties may see the other side as bluffing or completely unwilling to be reasonable, based on the self-serving interpretation of the ambiguous evidence.

In one experiment, subjects played the role of either the plaintiff or defendant in a hypothetical car accident case with a maximum potential damages payment of $100,000. The experiment used real money at the rate of $1 real = $10,000 experiment.

They then tried to settle in a fixed amount of time, and if they failed, the settlement amount would be charged a hefty legal bill. On average, plaintiffs thought the likely award would be $14,500 higher than the defendants. The further away the perceived “fair” figures were from each other strongly correlated with whether they could reach an agreement in time.

The self-serving bias, interestingly, seems not to exist in our struggles with personal computers. When we can’t get them to work, we blame ourselves rather than the technology. The reason is that people are so used to bad functionality, counterintuitive features, bugs, and sudden crashes of most contemporary software applications that they tend not to complain about them. Instead, they believe it is their personal responsibility to predict possible issues and to find solutions to computer problems. This unique phenomenon has been recently observed in several human-computer interaction investigations.


Semmelweis Reflex

Dr. Ignatz Semmelweis, assistant to the head of obstetrics at the Vienna General Hospital in the 1840s, discovered that his clinic, where doctors were trained, had a maternal mortality rate from puerperal fever (childbed fever) that averaged 10 percent. A second clinic, which trained midwives, had a mortality rate of only four percent.

This was well known outside the hospital. Semmelweis described women begging on their knees to go to the midwives clinic rather than risk the care of doctors. This, Semmelweis said, “made me so miserable that life seemed worthless.” Semmelweis started a systematic analysis to find out the cause, ruling out overcrowding, climate, and other factors before the death of an old friend from a condition similar to puerperal fever after being accidentally cut with a student’s scalpel during an autopsy.

Semmelweis imagined that some sort of “cadaverous particles” might be responsible, germs being at that time unknown. Midwives, after all, didn’t perform autopsies. Accordingly, Semmelweis required doctors to wash their hands in a mild bleach solution after performing autopsies. Following the change in procedures, death rates in the doctors clinic dropped almost immediately to the levels of the midwives clinic.

This theory contradicted medical belief of the time, and Semmelweis eventually was disgraced, lost his job, began accusing his fellow physicians of murder, and eventually died in a mental institution, possibly after being beaten by a guard.

Hence the Semmelweis effect: normally described as a reflex-like rejection of new knowledge because it contradicts entrenched norms, beliefs or paradigms: the “automatic rejection of the obvious, without thought, inspection, or experiment.”

Some credit Robert Anton Wilson for the phrase. Timothy Leary defined it as, “Mob behavior found among primates and larval hominids on undeveloped planets, in which a discovery of important scientific fact is punished.”

I don’t agree. I think there’s something else going on here.

The Semmelweis effect, I think, relates more to the implied threat and criticism the new knowledge has for old behavior. Let’s go back to Semmelweis’ original discovery. If his hypothesis about hand washing is correct, it means that physicians have contributed to the deaths of thousands of patients. Who wants to think of himself or herself as a killer, however inadvertent?


The Semmelweis reflex is, I think, better stated as the human tendency to reject or challenge scientific or other factual information that portrays us in a negative light. In that sense, it’s related to the phenomenon of reactance, discussed earlier.

In this case, Semmelweis’s own reaction to discovering the mortality rate of his clinic might have been a tip-off. He was “so miserable that life seemed worthless.” In his own case, this drove him to perform research, but these other doctors can only accept or deny the results. It’s not unreasonable to expect a certain amount of hostile response, and calling people “murderers,” as Semmelweis did, is hardly likely to win friends and influence people.

You don’t have to look far to find contemporary illustrations, from tobacco executives aghast someone dared accuse them of making a faulty product to the notorious Ford Motor Company indifference to safety in designing the Ford Pinto. The people involved weren’t trying to be unethical or immoral; they were in the grips of denial triggered by the Semmelweis reflex. This denial was strong enough to make them ignore or trivialize evidence that in retrospect appears conclusive.

When you’re accused of fault, watch for the Semmelweis reflex in yourself. The natural first impulse is to deny or deflect, but the right practice is to examine and explore. Depending on what you find, you can select a more reasoned strategy.



Serial Position Effect


The serial position effect, coined by Hermann Ebbinghaus, refers to the finding that recall accuracy varies as a function of an item's position within a study list. When asked to recall a list of items in any order (free recall), people tend to begin recall with the end of the list, recalling those items best (the recency effect). Among earlier list items, the first few items are recalled more frequently than the middle items (the primacy effect).

One suggested reason for the primacy effect is that the initial items presented are most effectively stored in long-term memory because of the greater amount of processing devoted to them. (The first list item can be rehearsed by itself; the second must be rehearsed along with the first, the third along with the first and second, and so on.) One suggested reason for the recency effect is that these items are still present in working memory when recall is solicited. Items that benefit from neither (the middle items) are recalled most poorly.

There is experimental support for these explanations. For example:

• The primacy effect (but not the recency effect) is reduced when items are presented quickly and is enhanced when presented slowly (factors that reduce and enhance processing of each item and thus permanent storage).
• The recency effect (but not the primacy effect) is reduced when an interfering task is given; for example, subjects may be asked to compute a math problem in their heads prior to recalling list items; this task requires working memory and interferes with any list items being attended to.
• Amnesiacs with poor ability to form permanent long-term memories do not show a primacy effect, but do show a recency effect.

More next week.

To read the whole series, click "Cognitive bias" in the tag cloud to your right, or search for any individual bias the same way.

See No Evil (Part 17 of Cognitive Biases)

Our 17th installment features the selection bias, selective perception in general, and the self-fulfilling prophecy.


Selection Bias

There’s a growing argument that telephone polls, once the gold standard of scientific opinion surveys, are becoming less reliable. More and more people are refusing to participate, meaning that the actual sample becomes to some extent self-selected: a random sample of people who like to take polls. People who don’t like to take polls are underrepresented in the results, and there’s no guarantee that class feels the same as the class answering.

Selection bias can happen in any scientific study requiring a statistical sample that is representative of some larger population: if the selection is flawed, and if other statistical analysis does not correct for the skew, the conclusions are not reliable.

There are several types of selection bias:

• Sampling bias. Systemic error resulting from a non-random population sample. Examples include self-selection, pre-screening, and discounting test subjects that don’t finish.
• Time interval bias. Error resulting from a flawed selection of the time interval. Examples include starting on an unusually low year and ending on an unusually high one, terminating a trial early when its results support your desired conclusion or favoring larger or shorter intervals in measuring change.
• Exposure bias. Error resulting from amplifying trends. When one disease predisposes someone for a second disease, the treatment for the first disease can appear correlated with the appearance of the second disease. An effective but not perfect treatment given to people at high risk of getting a particular disease could potentially result in the appearance of the treatment causing the disease, since the high-risk population would naturally include a higher number of people who got the treatment and the disease.
• Data bias. Rejection of “bad” data on arbitrary grounds, ignoring or discounting outliers, partitioning data with knowledge of the partitions, then analyzing them with tests designed for blindly chosen ones.
• Studies bias. Earlier, we looked at publication bias, the tendency to publish studies with positive results and ignore ones with negative results. If you put together a meta-analysis without correcting for publication bias, you’ve got a studies bias. Or you can perform repeated experiments and report only the favorable results, classifying the others as calibration tests or preliminary studies.
• Attrition bias. A selection bias resulting from people dropping out of a study over time. If you study the effectiveness of a weight loss program only by measuring outcomes for people who complete the whole program, it’ll often look very effective indeed — but it ignores the potentially vast number of people who tried and gave up.

In general, you can’t overcome a selection biases with statistical analysis of existing data alone. Informal workarounds examine correlations between background variables and a treatment indicator, but what’s missing is the correlation between unobserved determinants of the outcome and unobserved determinants of selection into the sample that create the bias. What you don’t see doesn’t have to be identical to what you do see.


Selective Perception

Expectations affect perception.

We know people are suggestible: several studies have shown that students who were told they were consuming alcohol when they weren’t still got drunk enough their driving was affected.

In one classic study, viewers watched a filmstrip of a particularly violent Princeton-Dartmouth football game. Princeton viewers reported seeing nearly twice as many rule infractions committed by the Dartmouth team than did Dartmouth viewers. One Dartmouth alumnus did not see any infractions committed by the Dartmouth side and sent a message that he’d only seen part of the film and wanted the rest.

Selective perception is also an issue for advertisers, as consumers may engage with some ads and not others based on their pre-existing beliefs about the brand. Seymour Smith, a prominent advertising researcher, found evidence for selective perception in advertising research in the early 1960s. People who like, buy, or are considering buying a brand are more likely to notice advertising than are those who are neutral toward the brand. It’s hard to measure the quality of the advertising if the only people who notice it are already predisposed to like the brand.



Self-Fulfilling Prophecy

A self-fulfilling prophecy is a prediction that directly or indirectly causes itself to become true, by the very terms of the prophecy itself, due to positive feedback between belief and behavior. The term was coined by sociologist Robert K. Merton, who formalized its structure and consequences in his 1949 book Social Theory and Social Structure.

A self-fulfilling prophecy is initially false: it becomes true by evoking the behavior that makes it come true. The actual course of events is offered as proof that the prophecy was originally true.

Self-fulfilling prophecies have been used in education as a type of placebo effect.

The effects of teacher attitudes, beliefs and values, affecting their expectations have been tested repeatedly. A famous example includes a study where teachers were told arbitrarily that random students were "going to blossom". The prophecy indeed self-fulfilled:  those random students actually ended the year with significantly greater improvements.


For previous installments, click on "Cognitive Bias" in the tag cloud to your right.