Placebo

From Wikipedia, the free encyclopedia

 

Placebos are typically inert tablets, such as sugar pills

 

A placebo (/pləˈsiːboʊ/ plə-SEE-boh) is an inert substance or treatment which is designed to have no therapeutic value. Common placebos include inert tablets (like sugar pills), inert injections (like saline), sham surgery, and other procedures.

In general, placebos can affect how patients perceive their condition and encourage the body's chemical processes for relieving pain and a few other symptoms, but have no impact on the disease itself. Improvements that patients experience after being treated with a placebo can also be due to unrelated factors, such as regression to the mean (a natural recovery from the illness). The use of placebos as treatment in clinical medicine raises ethical concerns, as it introduces dishonesty into the doctor–patient relationship.

In drug testing and medical research, a placebo can be made to resemble an active medication or therapy so that it functions as a control; this is to prevent the recipient or others from knowing (with their consent) whether a treatment is active or inactive, as expectations about efficacy can influence results. In a clinical trial any change in the placebo arm is known as the placebo response, and the difference between this and the result of no treatment is the placebo effect.

The idea of a placebo effect—a therapeutic outcome derived from an inert treatment—was discussed in 18th century psychology but became more prominent in the 20th century. An influential 1955 study entitled The Powerful Placebo firmly established the idea that placebo effects were clinically important, and were a result of the brain's role in physical health. A 1997 reassessment found no evidence of any placebo effect in the source data, as the study had not accounted for regression to the mean.

Definitions

The word "placebo", Latin for "I will please", dates back to a Latin translation of the Bible by St Jerome.

The American Society of Pain Management Nursing define a placebo as "any sham medication or procedure designed to be void of any known therapeutic value".

In a clinical trial, a placebo response is the measured response of subjects to a placebo; the placebo effect is the difference between that response and no treatment. It is also part of the recorded response to any active medical intervention.

Any measurable placebo effect is termed either objective (e.g. lowered blood pressure) or subjective (e.g. a lowered perception of pain).

Effects

Placebos can improve patient-reported outcomes such as pain and nausea. This effect is unpredictable and hard to measure, even in the best conducted trials. For example, if used to treat insomnia, placebos can cause patients to perceive that they are sleeping better, but do not improve objective measurements of sleep onset latency. A 2001 Cochrane Collaboration meta-analysis of the placebo effect looked at trials in 40 different medical conditions, and concluded the only one where it had been shown to have a significant effect was for pain.

By contrast, placebos do not appear to affect the actual diseases, or outcomes that are not dependent on a patient's perception. One exception to the latter is Parkinson's disease, where recent research has linked placebo interventions to improved motor functions.

Measuring the extent of the placebo effect is difficult due to confounding factors. For example, a patient may feel better after taking a placebo due to regression to the mean (i.e. a natural recovery or change in symptoms). It is harder still to tell the difference between the placebo effect and the effects of response bias, observer bias and other flaws in trial methodology, as a trial comparing placebo treatment and no treatment will not be a blinded experiment. In their 2010 meta-analysis of the placebo effect, Asbjørn Hróbjartsson and Peter C. Gøtzsche argue that "even if there were no true effect of placebo, one would expect to record differences between placebo and no-treatment groups due to bias associated with lack of blinding."

Hróbjartsson and Gøtzsche concluded that their study "did not find that placebo interventions have important clinical effects in general." Jeremy Howick has argued that combining so many varied studies to produce a single average might obscure that "some placebos for some things could be quite effective." To demonstrate this, he participated in a systematic review comparing active treatments and placebos using a similar method, which generated a clearly misleading conclusion that there is "no difference between treatment and placebo effects".

Factors influencing the power of the placebo effect

Louis Lasagna helped make placebo-controlled trials a standard practice in the U.S.. He also believed "warmth, sympathy, and understanding" had therapeutic benefits.

 

A review published in JAMA Psychiatry found that, in trials of antipsychotic medications, the change in response to receiving a placebo had increased significantly between 1960 and 2013. The review's authors identified several factors that could be responsible for this change, including inflation of baseline scores and enrollment of fewer severely ill patients. Another analysis published in Pain in 2015 found that placebo responses had increased considerably in neuropathic pain clinical trials conducted in the United States from 1990 to 2013. The researchers suggested that this may be because such trials have "increased in study size and length" during this time period.

Children seem to have greater response than adults to placebos.

Some studies have investigated the use of placebos where the patient is fully aware that the treatment is inert, known as an open-label placebo. A May 2017 meta-analysis found some evidence that open-label placebos have positive effects in comparison to no treatment, but said the result should be treated with "caution" and that further trials were needed.

Symptoms and conditions

A 2010 Cochrane Collaboration review suggests that placebo effects are apparent only in subjective, continuous measures, and in the treatment of pain and related conditions.

Pain

Placebos are believed to be capable of altering a person's perception of pain. "A person might reinterpret a sharp pain as uncomfortable tingling."

One way in which the magnitude of placebo analgesia can be measured is by conducting "open/hidden" studies, in which some patients receive an analgesic and are informed that they will be receiving it (open), while others are administered the same drug without their knowledge (hidden). Such studies have found that analgesics are considerably more effective when the patient knows they are receiving them.

Depression

In 2008, a controversial meta-analysis led by psychologist Irving Kirsch, analyzing data from the FDA, concluded that 82% of the response to antidepressants was accounted for by placebos. However, there are serious doubts about the used methods and the interpretation of the results, especially the use of 0.5 as cut-off point for the effect-size. A complete reanalysis and recalculation based on the same FDA data discovered that the Kirsch study suffered from "important flaws in the calculations". The authors concluded that although a large percentage of the placebo response was due to expectancy, this was not true for the active drug. Besides confirming drug effectiveness, they found that the drug effect was not related to depression severity.

Another meta-analysis found that 79% of depressed patients receiving placebo remained well (for 12 weeks after an initial 6–8 weeks of successful therapy) compared to 93% of those receiving antidepressants. In the continuation phase however, patients on placebo relapsed significantly more often than patients on antidepressants.

Negative effects

A phenomenon opposite to the placebo effect has also been observed. When an inactive substance or treatment is administered to a recipient who has an expectation of it having a negative impact, this intervention is known as a nocebo (Latin nocebo = "I shall harm"). A nocebo effect occurs when the recipient of an inert substance reports a negative effect or a worsening of symptoms, with the outcome resulting not from the substance itself, but from negative expectations about the treatment.

Another negative consequence is that placebos can cause side-effects associated with real treatment.

Withdrawal symptoms can also occur after placebo treatment. This was found, for example, after the discontinuation of the Women's Health Initiative study of hormone replacement therapy for menopause. Women had been on placebo for an average of 5.7 years. Moderate or severe withdrawal symptoms were reported by 4.8% of those on placebo compared to 21.3% of those on hormone replacement.

Ethics

In research trials

Knowingly giving a person a placebo when there is an effective treatment available is a bioethically complex issue. While placebo-controlled trials might provide information about the effectiveness of a treatment, it denies some patients what could be the best available (if unproven) treatment. Informed consent is usually required for a study to be considered ethical, including the disclosure that some test subjects will receive placebo treatments.

The ethics of placebo-controlled studies have been debated in the revision process of the Declaration of Helsinki. Of particular concern has been the difference between trials comparing inert placebos with experimental treatments, versus comparing the best available treatment with an experimental treatment; and differences between trials in the sponsor's developed countries versus the trial's targeted developing countries.

Some suggest that existing medical treatments should be used instead of placebos, to avoid having some patients not receive medicine during the trial.

In medical practice

The practice of doctors prescribing placebos that are disguised as real medication is controversial. A chief concern is that it is deceptive and could harm the doctor–patient relationship in the long run. While some say that blanket consent, or the general consent to unspecified treatment given by patients beforehand, is ethical, others argue that patients should always obtain specific information about the name of the drug they are receiving, its side effects, and other treatment options. This view is shared by some on the grounds of patient autonomy. There are also concerns that legitimate doctors and pharmacists could open themselves up to charges of fraud or malpractice by using a placebo. Critics also argued that using placebos can delay the proper diagnosis and treatment of serious medical conditions.

About 25% of physicians in both the Danish and Israeli studies used placebos as a diagnostic tool to determine if a patient's symptoms were real, or if the patient was malingering. Both the critics and the defenders of the medical use of placebos agreed that this was unethical. The British Medical Journal editorial said, "That a patient gets pain relief from a placebo does not imply that the pain is not real or organic in origin ...the use of the placebo for 'diagnosis' of whether or not pain is real is misguided." A survey in the United States of more than 10,000 physicians came to the result that while 24% of physicians would prescribe a treatment that is a placebo simply because the patient wanted treatment, 58% would not, and for the remaining 18%, it would depend on the circumstances.

Referring specifically to homeopathy, the House of Commons of the United Kingdom Science and Technology Committee has stated:

In the Committee's view, homeopathy is a placebo treatment and the Government should have a policy on prescribing placebos. The Government is reluctant to address the appropriateness and ethics of prescribing placebos to patients, which usually relies on some degree of patient deception. Prescribing of placebos is not consistent with informed patient choice—which the Government claims is very important—as it means patients do not have all the information needed to make choice meaningful. A further issue is that the placebo effect is unreliable and unpredictable.

In his 2008 book Bad Science, Ben Goldacre argues that instead of deceiving patients with placebos, doctors should use the placebo effect to enhance effective medicines. Edzard Ernst has argued similarly that "As a good doctor you should be able to transmit a placebo effect through the compassion you show your patients." In an opinion piece about homeopathy, Ernst argued that it is wrong to approve an ineffective treatment on the basis that it can make patients feel better through the placebo effect. His concerns are that it is deceitful and that the placebo effect is unreliable. Goldacre also concludes that the placebo effect does not justify the use of alternative medicine.

Mechanisms

Expectation plays a clear role. A placebo presented as a stimulant may trigger an effect on heart rhythm and blood pressure, but when administered as a depressant, the opposite effect.

Psychology

The "placebo effect" may be related to expectations

 

In psychology, the two main hypotheses of placebo effect are expectancy theory and classical conditioning.

In 1985, Irving Kirsch hypothesized that placebo effects are produced by the self-fulfilling effects of response expectancies, in which the belief that one will feel different leads a person to actually feel different. According to this theory, the belief that one has received an active treatment can produce the subjective changes thought to be produced by the real treatment. Placebos can act similarly through classical conditioning, wherein a placebo and an actual stimulus are used simultaneously until the placebo is associated with the effect from the actual stimulus. Both conditioning and expectations play a role in placebo effect, and make different kinds of contribution. Conditioning has a longer-lasting effect, and can affect earlier stages of information processing. Those who think a treatment will work display a stronger placebo effect than those who do not, as evidenced by a study of acupuncture.

Additionally, motivation may contribute to the placebo effect. The active goals of an individual changes their somatic experience by altering the detection and interpretation of expectation-congruent symptoms, and by changing the behavioral strategies a person pursues. Motivation may link to the meaning through which people experience illness and treatment. Such meaning is derived from the culture in which they live and which informs them about the nature of illness and how it responds to treatment.

Placebo analgesia

Functional imaging upon placebo analgesia suggests links to the activation, and increased functional correlation between this activation, in the anterior cingulate, prefrontal, orbitofrontal and insular cortices, nucleus accumbens, amygdala, the brainstem periaqueductal gray matter, and the spinal cord.

It has been known that placebo analgesia depends upon the release in the brain of endogenous opioids since 1978. Such analgesic placebos activation changes processing lower down in the brain by enhancing the descending inhibition through the periaqueductal gray on spinal nociceptive reflexes, while the expectations of anti-analgesic nocebos acts in the opposite way to block this.

Functional imaging upon placebo analgesia has been summarized as showing that the placebo response is "mediated by "top-down" processes dependent on frontal cortical areas that generate and maintain cognitive expectancies. Dopaminergic reward pathways may underlie these expectancies". "Diseases lacking major 'top-down' or cortically based regulation may be less prone to placebo-related improvement".

Brain and body

In conditioning, a neutral stimulus saccharin is paired in a drink with an agent that produces an unconditioned response. For example, that agent might be cyclophosphamide, which causes immunosuppression. After learning this pairing, the taste of saccharin by itself is able to cause immunosuppression, as a new conditioned response via neural top-down control. Such conditioning has been found to affect a diverse variety of not just basic physiological processes in the immune system but ones such as serum iron levels, oxidative DNA damage levels, and insulin secretion. Recent reviews have argued that the placebo effect is due to top-down control by the brain for immunity and pain. Pacheco-López and colleagues have raised the possibility of "neocortical-sympathetic-immune axis providing neuroanatomical substrates that might explain the link between placebo/conditioned and placebo/expectation responses." There has also been research aiming to understand underlying neurobiological mechanisms of action in pain relief, immunosuppression, Parkinson's disease and depression.

Dopaminergic pathways have been implicated in the placebo response in pain and depression.

Confounding factors

Placebo-controlled studies, as well as studies of the placebo effect itself, often fail to adequately identify confounding factors. False impressions of placebo effects are caused by many factors including:

• Regression to the mean (natural recovery or fluctuation of symptoms)
• Additional treatments
• Response bias from subjects, including scaling bias, answers of politeness, experimental subordination, conditioned answers;
• Reporting bias from experimenters, including misjudgment and irrelevant response variables.
• Non-inert ingredients of the placebo medication having an unintended physical effect

History

A quack treating a patient with Perkins Patent Tractors by James Gillray, 1801. John Haygarth used this remedy to illustrate the power of the placebo effect.

 

The word placebo was used in a medicinal context in the late 18th century to describe a "commonplace method or medicine" and in 1811 it was defined as "any medicine adapted more to please than to benefit the patient". Although this definition contained a derogatory implication it did not necessarily imply that the remedy had no effect.

Placebos have featured in medical use until well into the twentieth century. In 1955 Henry K. Beecher published an influential paper entitled The Powerful Placebo which proposed idea that placebo effects were clinically important. Subsequent re-analysis of his materials, however, found in them no evidence of any "placebo effect".

Placebo-controlled studies

The placebo effect makes it more difficult to evaluate new treatments. Clinical trials control for this effect by including a group of subjects that receives a sham treatment. The subjects in such trials are blinded as to whether they receive the treatment or a placebo. If a person is given a placebo under one name, and they respond, they will respond in the same way on a later occasion to that placebo under that name but not if under another.

Clinical trials are often double-blinded so that the researchers also do not know which test subjects are receiving the active or placebo treatment. The placebo effect in such clinical trials is weaker than in normal therapy since the subjects are not sure whether the treatment they are receiving is active.

Hawthorne effect

From Wikipedia, the free encyclopedia

The Hawthorne effect (also referred to as the observer effect) is a type of reactivity in which individuals modify an aspect of their behavior in response to their awareness of being observed. This can undermine the integrity of research, particularly the relationships between variables.

The original research at the Hawthorne Works in Cicero, Illinois, on lighting changes and work structure changes such as working hours and break times was originally interpreted by Elton Mayo and others to mean that paying attention to overall worker needs would improve productivity.

Later interpretations such as that done by Landsberger suggested that the novelty of being research subjects and the increased attention from such could lead to temporary increases in workers' productivity. This interpretation was dubbed "the Hawthorne effect". It is also similar to a phenomenon that is referred to as novelty/disruption effect.

History

Aerial view of the Hawthorne Works, ca. 1925

 

The term was coined in 1958 by Henry A. Landsberger when he was analyzing earlier experiments from 1924–32 at the Hawthorne Works (a Western Electric factory outside Chicago). The Hawthorne Works had commissioned a study to see if its workers would become more productive in higher or lower levels of light. The workers' productivity seemed to improve when changes were made, and slumped when the study ended. It was suggested that the productivity gain occurred as a result of the motivational effect on the workers of the interest being shown in them.

This effect was observed for minute increases in illumination. In these lighting studies, light intensity was altered to examine its effect on worker productivity. Most industrial/occupational psychology and organizational behavior textbooks refer to the illumination studies. Only occasionally are the rest of the studies mentioned.

Although illumination research of workplace lighting formed the basis of the Hawthorne effect, other changes such as maintaining clean work stations, clearing floors of obstacles, and even relocating workstations resulted in increased productivity for short periods. Thus the term is used to identify any type of short-lived increase in productivity.

Relay assembly experiments

In one of the studies, researchers chose two women as test subjects and asked them to choose four other workers to join the test group. Together the women worked in a separate room over the course of five years (1927–1932) assembling telephone relays.

Output was measured mechanically by counting how many finished relays each worker dropped down a chute. This measuring began in secret two weeks before moving the women to an experiment room and continued throughout the study. In the experiment room they had a supervisor who discussed changes with their productivity. Some of the variables were:

• Giving two 5-minute breaks (after a discussion with them on the best length of time), and then changing to two 10-minute breaks (not their preference). Productivity increased, but when they received six 5-minute rests, they disliked it and reduced output.
• Providing food during the breaks.
• Shortening the day by 30 minutes (output went up); shortening it more (output per hour went up, but overall output decreased); returning to the first condition (where output peaked).

Changing a variable usually increased productivity, even if the variable was just a change back to the original condition. However it is said that this is the natural process of the human being adapting to the environment, without knowing the objective of the experiment occurring. Researchers concluded that the workers worked harder because they thought that they were being monitored individually.

Researchers hypothesized that choosing one's own coworkers, working as a group, being treated as special (as evidenced by working in a separate room), and having a sympathetic supervisor were the real reasons for the productivity increase. One interpretation, mainly due to Elton Mayo, was that "the six individuals became a team and the team gave itself wholeheartedly and spontaneously to cooperation in the experiment." (There was a second relay assembly test room study whose results were not as significant as the first experiment.)

Bank wiring room experiments

The purpose of the next study was to find out how payment incentives would affect productivity. The surprising result was that productivity actually decreased. Workers apparently had become suspicious that their productivity may have been boosted to justify firing some of the workers later on. The study was conducted by Elton Mayo and W. Lloyd Warner between 1931 and 1932 on a group of fourteen men who put together telephone switching equipment. The researchers found that although the workers were paid according to individual productivity, productivity decreased because the men were afraid that the company would lower the base rate. Detailed observation of the men revealed the existence of informal groups or "cliques" within the formal groups. These cliques developed informal rules of behavior as well as mechanisms to enforce them. The cliques served to control group members and to manage bosses; when bosses asked questions, clique members gave the same responses, even if they were untrue. These results show that workers were more responsive to the social force of their peer groups than to the control and incentives of management.

Interpretation and criticism

Richard Nisbett has described the Hawthorne effect as "a glorified anecdote", saying that "once you have got the anecdote, you can throw away the data." Other researchers have attempted to explain the effects with various interpretations.

Adair warns of gross factual inaccuracy in most secondary publications on Hawthorne effect and that many studies failed to find it. He argues that it should be viewed as a variant of Orne's (1973) experimental demand effect. So for Adair, the issue is that an experimental effect depends on the participants' interpretation of the situation; this is why manipulation checks are important in social sciences experiments. So he thinks it is not awareness per se, nor special attention per se, but participants' interpretation that must be investigated in order to discover if/how the experimental conditions interact with the participants' goals. This can affect whether participants believe something, if they act on it or do not see it as in their interest, etc.

Possible explanations for the Hawthorne effect include the impact of feedback and motivation towards the experimenter. Receiving feedback on their performance may improve their skills when an experiment provides this feedback for the first time. Research on the demand effect also suggests that people may be motivated to please the experimenter, at least if it does not conflict with any other motive. They may also be suspicious of the purpose of the experimenter. Therefore, Hawthorne effect may only occur when there is usable feedback or a change in motivation.

Parsons defines the Hawthorne effect as "the confounding that occurs if experimenters fail to realize how the consequences of subjects' performance affect what subjects do" [i.e. learning effects, both permanent skill improvement and feedback-enabled adjustments to suit current goals]. His key argument is that in the studies where workers dropped their finished goods down chutes, the participants had access to the counters of their work rate.

Mayo contended that the effect was due to the workers reacting to the sympathy and interest of the observers. He does say that this experiment is about testing overall effect, not testing factors separately. He also discusses it not really as an experimenter effect but as a management effect: how management can make workers perform differently because they feel differently. A lot to do with feeling free, not feeling supervised but more in control as a group. The experimental manipulations were important in convincing the workers to feel this way: that conditions were really different. The experiment was repeated with similar effects on mica-splitting workers.

Clark and Sugrue in a review of educational research say that uncontrolled novelty effects cause on average 30% of a standard deviation (SD) rise (i.e. 50%–63% score rise), which decays to small level after 8 weeks. In more detail: 50% of a SD for up to 4 weeks; 30% of SD for 5–8 weeks; and 20% of SD for > 8 weeks, (which is < 1% of the variance).

Harry Braverman points out that the Hawthorne tests were based on industrial psychology and were investigating whether workers' performance could be predicted by pre-hire testing. The Hawthorne study showed "that the performance of workers had little relation to ability and in fact often bore an inverse relation to test scores...". Braverman argues that the studies really showed that the workplace was not "a system of bureaucratic formal organisation on the Weberian model, nor a system of informal group relations, as in the interpretation of Mayo and his followers but rather a system of power, of class antagonisms". This discovery was a blow to those hoping to apply the behavioral sciences to manipulate workers in the interest of management.

The economists Steven Levitt and John A. List long pursued without success a search for the base data of the original illumination experiments, before finding it in a microfilm at the University of Wisconsin in Milwaukee in 2011. Re-analysing it, they found slight evidence for the Hawthorn effect over the long-run, but in no way as drastic as suggested initially. This finding supported the analysis of an article by S R G Jones in 1992 examining the relay experiments. Despite the absence of evidence for the Hawthorne Effect in the original study, List has said that he remains confident that the effect is genuine.

It is also possible that the illumination experiments can be explained by a longitudinal learning effect. Parsons has declined to analyse the illumination experiments, on the grounds that they have not been properly published and so he cannot get at details, whereas he had extensive personal communication with Roethlisberger and Dickson.

Evaluation of the Hawthorne effect continues in the present day. Despite the criticisms, however, the phenomenon is often taken into account when designing studies and their conclusions. Some have also developed ways to avoid it. For instance, there is the case of holding the observation when conducting a field study from a distance, from behind a barrier such as a two-way mirror or using an unobtrusive measure.

Trial effect

Various medical scientists have studied possible trial effect (clinical trial effect) in clinical trials. Some postulate that, beyond just attention and observation, there may be other factors involved, such as slightly better care; slightly better compliance/adherence; and selection bias. The latter may have several mechanisms: (1) Physicians may tend to recruit patients who seem to have better adherence potential and lesser likelihood of future loss to follow-up. (2) The inclusion/exclusion criteria of trials often exclude at least some comorbidities; although this is often necessary to prevent confounding, it also means that trials may tend to work with healthier patient subpopulations.

Secondary observer effect

Despite the observer effect as popularized in the Hawthorne experiments being perhaps falsely identified (see above discussion), the popularity and plausibility of the observer effect in theory has led researchers to postulate that this effect could take place at a second level. Thus it has been proposed that there is a secondary observer effect when researchers working with secondary data such as survey data or various indicators may impact the results of their scientific research. Rather than having an effect on the subjects (as with the primary observer effect), the researchers likely have their own idiosyncrasies that influence how they handle the data and even what data they obtain from secondary sources. For one, the researchers may choose seemingly innocuous steps in their statistical analyses that end up causing significantly different results using the same data; e.g., weighting strategies, factor analytic techniques, or choice of estimation. In addition, researchers may use software packages that have different default settings that lead to small but significant fluctuations. Finally, the data that researchers use may not be identical, even though it seems so. For example, the OECD collects and distributes various socio-economic data; however, these data change over time such that a researcher who downloads the Australian GDP data for the year 2000 may have slightly different values than a researcher who downloads the same Australian GDP 2000 data a few years later. The idea of the secondary observer effect was floated by Nate Breznau in a thus far relatively obscure paper.

Although little attention has been paid to this phenomenon, the scientific implications are very large. Evidence of this effect may be seen in recent studies that assign a particular problem to a number of researchers or research teams who then work independently using the same data to try and find a solution. This is a process called crowdsourcing data analysis and was used in a groundbreaking study by Silberzahn, Rafael, Eric Uhlmann, Dan Martin and Brian Nosek et al. (2015) about red cards and player race in football (i.e., soccer).

Illusory correlation

From Wikipedia, the free encyclopedia

In psychology, illusory correlation is the phenomenon of perceiving a relationship between variables (typically people, events, or behaviors) even when no such relationship exists. A false association may be formed because rare or novel occurrences are more salient and therefore tend to capture one's attention. This phenomenon is one way stereotypes form and endure. Hamilton & Rose (1980) found that stereotypes can lead people to expect certain groups and traits to fit together, and then to overestimate the frequency with which these correlations actually occur.

History

"Illusory correlation" was originally coined by Chapman and Chapman (1967) to describe people's tendencies to overestimate relationships between two groups when distinctive and unusual information is presented. The concept was used to question claims about objective knowledge in clinical psychology through Chapmans' refutation of many clinicians' widely used Wheeler signs for homosexuality in Rorschach tests.

Example

David Hamilton and Robert Gifford (1976) conducted a series of experiments that demonstrated how stereotypic beliefs regarding minorities could derive from illusory correlation processes. To test their hypothesis, Hamilton and Gifford had research participants read a series of sentences describing either desirable or undesirable behaviors, which were attributed to either Group A or Group B. Abstract groups were used so that no previously established stereotypes would influence results. Most of the sentences were associated with Group A, and the remaining few were associated with Group B. The following table summarizes the information given.

Behaviors	Group A (majority)	Group B (minority)	Total
Desirable	18 (69%)	9 (69%)	27
Undesirable	8 (30%)	4 (30%)	12
Total	26	13	39

Each group had the same proportions of positive and negative behaviors, so there was no real association between behaviors and group membership. Results of the study show that positive, desirable behaviors were not seen as distinctive so people were accurate in their associations. On the other hand, when distinctive, undesirable behaviors were represented in the sentences, the participants overestimated how much the minority group exhibited the behaviors.

A parallel effect occurs when people judge whether two events, such as pain and bad weather, are correlated. They rely heavily on the relatively small number of cases where the two events occur together. People pay relatively little attention to the other kinds of observation (of no pain or good weather).

Theories

General theory

Most explanations for illusory correlation involve psychological heuristics: information processing short-cuts that underlie many human judgments. One of these is availability: the ease with which an idea comes to mind. Availability is often used to estimate how likely an event is or how often it occurs. This can result in illusory correlation, because some pairings can come easily and vividly to mind even though they are not especially frequent.

Information processing

Martin Hilbert (2012) proposes an information processing mechanism that assumes a noisy conversion of objective observations into subjective judgments. The theory defines noise as the mixing of these observations during retrieval from memory. According to the model, underlying cognitions or subjective judgments are identical with noise or objective observations that can lead to overconfidence or what is known as conservatism bias—when asked about behavior participants underestimate the majority or larger group and overestimate the minority or smaller group. These results are illusory correlations.

Working-memory capacity

In an experimental study done by Eder, Fiedler and Hamm-Eder (2011), the effects of working-memory capacity on illusory correlations were investigated. They first looked at the individual differences in working memory, and then looked to see if that had any effect on the formation of illusory correlations. They found that individuals with higher working memory capacity viewed minority group members more positively than individuals with lower working memory capacity. In a second experiment, the authors looked into the effects of memory load in working memory on illusory correlations. They found that increased memory load in working memory led to an increase in the prevalence of illusory correlations. The experiment was designed to specifically test working memory and not substantial stimulus memory. This means that the development of illusory correlations was caused by deficiencies in central cognitive resources caused by the load in working memory, not selective recall.

Attention theory of learning

Attention theory of learning proposes that features of majority groups are learned first, and then features of minority groups. This results in an attempt to distinguish the minority group from the majority, leading to these differences being learned more quickly. The Attention theory also argues that, instead of forming one stereotype regarding the minority group, two stereotypes, one for the majority and one for the minority, are formed.

Effect of learning

A study was conducted to investigate whether increased learning would have any effect on illusory correlations. It was found that educating people about how illusory correlation occurs resulted in a decreased incidence of illusory correlations.

Age

Johnson and Jacobs (2003) performed an experiment to see how early in life individuals begin forming illusory correlations. Children in grades 2 and 5 were exposed to a typical illusory correlation paradigm to see if negative attributes were associated with the minority group. The authors found that both groups formed illusory correlations.

A study also found that children create illusory correlations. In their experiment, children in grades 1, 3, 5, and 7, and adults all looked at the same illusory correlation paradigm. The study found that children did create significant illusory correlations, but those correlations were weaker than the ones created by adults. In a second study, groups of shapes with different colors were used. The formation of illusory correlation persisted showing that social stimuli are not necessary for creating these correlations.

Explicit versus implicit attitudes

Two studies performed by Ratliff and Nosek examined whether or not explicit and implicit attitudes affected illusory correlations. In one study, Ratliff and Nosek had two groups: one a majority and the other a minority. They then had three groups of participants, all with readings about the two groups. One group of participants received overwhelming pro-majority readings, one was given pro-minority readings, and one received neutral readings. The groups that had pro-majority and pro-minority readings favored their respective pro groups both explicitly and implicitly. The group that had neutral readings favored the majority explicitly, but not implicitly. The second study was similar, but instead of readings, pictures of behaviors were shown, and the participants wrote a sentence describing the behavior they saw in the pictures presented. The findings of both studies supported the authors' argument that the differences found between the explicit and implicit attitudes is a result of the interpretation of the covariation and making judgments based on these interpretations (explicit) instead of just accounting for the covariation (implicit).

Paradigm structure

Berndsen et al. (1999) wanted to determine if the structure of testing for illusory correlations could lead to the formation of illusory correlations. The hypothesis was that identifying test variables as Group A and Group B might be causing the participants to look for differences between the groups, resulting in the creation of illusory correlations. An experiment was set up where one set of participants were told the groups were Group A and Group B, while another set of participants were given groups labeled as students who graduated in 1993 or 1994. This study found that illusory correlations were more likely to be created when the groups were Group A and B, as compared to students of the class of 1993 or the class of 1994.

Spurious relationship

From Wikipedia, the free encyclopedia

 

In statistics, a spurious relationship or spurious correlation is a mathematical relationship in which two or more events or variables are associated but not causally related, due to either coincidence or the presence of a certain third, unseen factor (referred to as a "common response variable", "confounding factor", or "lurking variable").

Examples

A well-known case of a spurious relationship can be found in the time-series literature, where a spurious regression is a regression that provides misleading statistical evidence of a linear relationship between independent non-stationary variables. In fact, the non-stationarity may be due to the presence of a unit root in both variables. In particular, any two nominal economic variables are likely to be correlated with each other, even when neither has a causal effect on the other, because each equals a real variable times the price level, and the common presence of the price level in the two data series imparts correlation to them.

An example of a spurious relationship can be seen by examining a city's ice cream sales. These sales are highest when the rate of drownings in city swimming pools is highest. To allege that ice cream sales cause drowning, or vice versa, would be to imply a spurious relationship between the two. In reality, a heat wave may have caused both. The heat wave is an example of a hidden or unseen variable, also known as a confounding variable.

Another commonly noted example is a series of Dutch statistics showing a positive correlation between the number of storks nesting in a series of springs and the number of human babies born at that time. Of course there was no causal connection; they were correlated with each other only because they were correlated with the weather nine months before the observations. However Höfer et al. (2004) showed the correlation to be stronger than just weather variations as he could show in post reunification Germany that, while the number of clinical deliveries was not linked with the rise in stork population, out of hospital deliveries correlated with the stork population.

In rare cases, a spurious relationship can occur between two completely unrelated variables without any confounding variable, as was the case between the success of the Washington Redskins professional football team in a specific game before each presidential election and the success of the incumbent President's political party in said election. For 16 consecutive elections between 1940 and 2000, the Redskins Rule correctly matched whether the incumbent President's political party would retain or lose the Presidency. The rule eventually failed shortly after Elias Sports Bureau discovered the correlation in 2000; in 2004, 2012 and 2016, the results of the Redskins game and the election did not match.

Hypothesis testing

Often one tests a null hypothesis of no correlation between two variables, and chooses in advance to reject the hypothesis if the correlation computed from a data sample would have occurred in less than (say) 5% of data samples if the null hypothesis were true. While a true null hypothesis will be accepted 95% of the time, the other 5% of the times having a true null of no correlation a zero correlation will be wrongly rejected, causing acceptance of a correlation which is spurious (an event known as Type I error). Here the spurious correlation in the sample resulted from random selection of a sample that did not reflect the true properties of the underlying population.

Detecting spurious relationships

The term "spurious relationship" is commonly used in statistics and in particular in experimental research techniques, both of which attempt to understand and predict direct causal relationships (X → Y). A non-causal correlation can be spuriously created by an antecedent which causes both (W → X and W → Y). Mediating variables, (X → W → Y), if undetected, estimate a total effect rather than direct effect without adjustment for the mediating variable M. Because of this, experimentally identified correlations do not represent causal relationships unless spurious relationships can be ruled out.

Experiments

In experiments, spurious relationships can often be identified by controlling for other factors, including those that have been theoretically identified as possible confounding factors. For example, consider a researcher trying to determine whether a new drug kills bacteria; when the researcher applies the drug to a bacterial culture, the bacteria die. But to help in ruling out the presence of a confounding variable, another culture is subjected to conditions that are as nearly identical as possible to those facing the first-mentioned culture, but the second culture is not subjected to the drug. If there is an unseen confounding factor in those conditions, this control culture will die as well, so that no conclusion of efficacy of the drug can be drawn from the results of the first culture. On the other hand, if the control culture does not die, then the researcher cannot reject the hypothesis that the drug is efficacious.

Non-experimental statistical analyses

Disciplines whose data are mostly non-experimental, such as economics, usually employ observational data to establish causal relationships. The body of statistical techniques used in economics is called econometrics. The main statistical method in econometrics is multivariable regression analysis. Typically a linear relationship such as

${\displaystyle y=a_{0}+a_{1}x_{1}+a_{2}x_{2}+\cdots +a_{k}x_{k}+e}$

is hypothesized, in which ${\displaystyle y}$ is the dependent variable (hypothesized to be the caused variable), ${\displaystyle x_{j}}$ for j = 1, ..., k is the j^th independent variable (hypothesized to be a causative variable), and ${\displaystyle e}$ is the error term (containing the combined effects of all other causative variables, which must be uncorrelated with the included independent variables). If there is reason to believe that none of the ${\displaystyle x_{j}}$s is caused by y, then estimates of the coefficients ${\displaystyle a_{j}}$ are obtained. If the null hypothesis that ${\displaystyle a_{j}=0}$ is rejected, then the alternative hypothesis that ${\displaystyle a_{j}\neq 0}$ and equivalently that ${\displaystyle x_{j}}$ causes y cannot be rejected. On the other hand, if the null hypothesis that ${\displaystyle a_{j}=0}$ cannot be rejected, then equivalently the hypothesis of no causal effect of ${\displaystyle x_{j}}$ on y cannot be rejected. Here the notion of causality is one of contributory causality: If the true value ${\displaystyle a_{j}\neq 0}$, then a change in ${\displaystyle x_{j}}$ will result in a change in y unless some other causative variable(s), either included in the regression or implicit in the error term, change in such a way as to exactly offset its effect; thus a change in ${\displaystyle x_{j}}$ is not sufficient to change y. Likewise, a change in ${\displaystyle x_{j}}$ is not necessary to change y, because a change in y could be caused by something implicit in the error term (or by some other causative explanatory variable included in the model). 

Regression analysis controls for other relevant variables by including them as regressors (explanatory variables). This helps to avoid mistaken inference of causality due to the presence of a third, underlying, variable that influences both the potentially causative variable and the potentially caused variable: its effect on the potentially caused variable is captured by directly including it in the regression, so that effect will not be picked up as a spurious effect of the potentially causative variable of interest. In addition, the use of multivariate regression helps to avoid wrongly inferring that an indirect effect of, say x₁ (e.g., x₁ → x₂ → y) is a direct effect (x₁ → y). 

Just as an experimenter must be careful to employ an experimental design that controls for every confounding factor, so also must the user of multiple regression be careful to control for all confounding factors by including them among the regressors. If a confounding factor is omitted from the regression, its effect is captured in the error term by default, and if the resulting error term is correlated with one (or more) of the included regressors, then the estimated regression may be biased or inconsistent (see omitted variable bias). 

In addition to regression analysis, the data can be examined to determine if Granger causality exists. The presence of Granger causality indicates both that x precedes y, and that x contains unique information about y.

Other relationships

There are several other relationships defined in statistical analysis as follows.

• Direct relationship
• Mediating relationship
• Moderating relationship

All models are wrong

From Wikipedia, the free encyclopedia

 

"All models are wrong" is a common aphorism in statistics; it is often expanded as "All models are wrong, but some are useful". It is usually considered to be applicable to not only statistical models, but to scientific models generally. The aphorism is generally attributed to the statistician George Box, although the underlying concept predates Box's writings.

Quotations of George Box

The first record of Box saying "all models are wrong" is in a 1976 paper published in the Journal of the American Statistical Association. The 1976 paper contains the aphorism twice. The two sections of the paper that contain the aphorism are copied below.

2.3  Parsimony
Since all models are wrong the scientist cannot obtain a "correct" one by excessive elaboration. On the contrary following William of Occam he should seek an economical description of natural phenomena. Just as the ability to devise simple but evocative models is the signature of the great scientist so overelaboration and overparameterization is often the mark of mediocrity.
2.4  Worrying Selectively
Since all models are wrong the scientist must be alert to what is importantly wrong. It is inappropriate to be concerned about mice when there are tigers abroad.

Box repeated the aphorism in a paper that was published in the proceedings of a 1978 statistics workshop. The paper contains a section entitled "All models are wrong but some are useful". The section is copied below.

Now it would be very remarkable if any system existing in the real world could be exactly represented by any simple model. However, cunningly chosen parsimonious models often do provide remarkably useful approximations. For example, the law PV = RT relating pressure P, volume V and temperature T of an "ideal" gas via a constant R is not exactly true for any real gas, but it frequently provides a useful approximation and furthermore its structure is informative since it springs from a physical view of the behavior of gas molecules.
For such a model there is no need to ask the question "Is the model true?". If "truth" is to be the "whole truth" the answer must be "No". The only question of interest is "Is the model illuminating and useful?".

Box repeated the aphorism twice more in his 1987 book, Empirical Model-Building and Response Surfaces (which was co-authored with Norman Draper). The first repetition is on p. 74: "Remember that all models are wrong; the practical question is how wrong do they have to be to not be useful." The second repetition is on p. 424, which is excerpted below.

... all models are approximations. Essentially, all models are wrong, but some are useful. However, the approximate nature of the model must always be borne in mind ...

A second edition of the book was published in 2007, under the title Response Surfaces, Mixtures, and Ridge Analyses. The second edition also repeats the aphorism twice, in contexts identical with those of the first edition (on p. 63 and p. 414).

Box repeated the aphorism two more times in his 1997 book, Statistical Control: By Monitoring and Feedback Adjustment (which was co-authored with Alberto Luceño). The first repetition is on p. 6, which is excerpted below.

It has been said that "all models are wrong but some models are useful." In other words, any model is at best a useful fiction—there never was, or ever will be, an exactly normal distribution or an exact linear relationship. Nevertheless, enormous progress has been made by entertaining such fictions and using them as approximations.

The second repetition is on p. 9: "So since all models are wrong, it is very important to know what to worry about; or, to put it in another way, what models are likely to produce procedures that work in practice (where exact assumptions are never true)".

A second edition of the book was published in 2009, under the title Statistical Control By Monitoring and Adjustment (co-authored with Alberto Luceño and Maria del Carmen Paniagua-Quiñones). The second edition also repeats the aphorism two times. The first repetition is on p. 61, which is excerpted below.

All models are approximations. Assumptions, whether implied or clearly stated, are never exactly true. All models are wrong, but some models are useful. So the question you need to ask is not "Is the model true?" (it never is) but "Is the model good enough for this particular application?"

The second repetition is on p. 63; its context is essentially the same as that of the second repetition in the first edition.

Box's widely cited book Statistics for Experimenters (co-authored with William Hunter) does not include the aphorism in its first edition (published in 1978). The second edition (published in 2005; co-authored with William Hunter and J. Stuart Hunter) includes the aphorism three times: on p. 208, p. 384, and p. 440. On p. 440, the relevant sentence is this: "The most that can be expected from any model is that it can supply a useful approximation to reality: All models are wrong; some models are useful". 

In addition to stating the aphorism verbatim, Box sometimes stated the essence of the aphorism with different words. One example is from 1978, while Box was President of the American Statistical Association. At the annual meeting of the Association, Box delivered his Presidential Address, wherein he stated this: "Models, of course, are never true, but fortunately it is only necessary that they be useful".

Discussions

There have been varied discussions about the aphorism. A selection from those discussions is presented below. 

In 1983, the statisticians Peter McCullagh and John Nelder published their much-cited book on generalized linear models. The book includes a brief discussion of the aphorism (though without citing Box). A second edition of the book, published in 1989, contains a very similar discussion of the aphorism. The discussion from the first edition is as follows.

Modelling in science remains, partly at least, an art. Some principles do exist, however, to guide the modeller. The first is that all models are wrong; some, though, are better than others and we can search for the better ones. At the same time we must recognize that eternal truth is not within our grasp.

In 1995, the statistician Sir David Cox commented as follows.

... it does not seem helpful just to say that all models are wrong. The very word model implies simplification and idealization. The idea that complex physical, biological or sociological systems can be exactly described by a few formulae is patently absurd. The construction of idealized representations that capture important stable aspects of such systems is, however, a vital part of general scientific analysis and statistical models, especially substantive ones, do not seem essentially different from other kinds of model.

In 1996, an Applied Statistician's Creed was proposed. The Creed includes, in its core part, the aphorism.

In 2002, K.P. Burnham and D.R. Anderson published their much-cited book on statistical model selection. The book states the following.

A model is a simplification or approximation of reality and hence will not reflect all of reality. ... Box noted that "all models are wrong, but some are useful." While a model can never be "truth," a model might be ranked from very useful, to useful, to somewhat useful to, finally, essentially useless.

The statistician J. Michael Steele has commented on the aphorism as follows.

... there are wonderful models — like city maps....
If I say that a map is wrong, it means that a building is misnamed, or the direction of a one-way street is mislabeled. I never expected my map to recreate all of physical reality, and I only feel ripped off if my map does not correctly answer the questions that it claims to answer.
My maps of Philadelphia are useful. Moreover, except for a few that are out-of-date, they are not wrong.
So, you say, "Yes, a map can be thought of as a model, but surely it would be more precise to say that a map is a 'visually enhanced database.' Such databases can be correct. These are not the kinds of models that Box had in mind."
I agree. ...

In 2008, the statistician Andrew Gelman responded to that, saying in particular the following.

I take his general point, which is that a street map could be exactly correct, to the resolution of the map.
... The saying, "all models are wrong," is helpful because it is not completely obvious....
This is a simple point, and I can see how Steele can be irritated by people making a big point about it. But, the trouble is, many people don't realize that all models are wrong.

In 2013, the philosopher of science Peter Truran published an essay related to the aphorism. The essay notes, in particular, the following.

... seemingly incompatible models may be used to make predictions about the same phenomenon. ... For each model we may believe that its predictive power is an indication of its being at least approximately true. But if both models are successful in making predictions, and yet mutually inconsistent, how can they both be true? Let us consider a simple illustration. Two observers are looking at a physical object. One may report seeing a circular disc, and the other may report seeing a rectangle. Both will be correct, but one will be looking at the object (a cylindrical can) from above and the other will be observing from the side. The two models represent different aspects of the same reality.

Truran's essay further notes that Newton's theory of gravitation has been supplanted by Einstein's theory of relativity and yet Newton's theory remains generally "empirically adequate". Indeed, Newton's theory generally has excellent predictive power. Yet Newton's theory is not an approximation of Einstein's theory. For illustration, consider an apple falling down from a tree. Under Newton's theory, the apple falls because Earth exerts a force on the apple—what is called "the force of gravity". Under Einstein's theory, Earth does not exert any force on the apple. Hence, Newton's theory might be regarded as being, in some sense, completely wrong but extremely useful. (The usefulness of Newton's theory comes partly from being vastly simpler, both mathematically and computationally, than Einstein's theory.)

In 2014, the statistician David Hand made the following statement.

In general, when building statistical models, we must not forget that the aim is to understand something about the real world. Or predict, choose an action, make a decision, summarize evidence, and so on, but always about the real world, not an abstract mathematical world: our models are not the reality—a point well made by George Box in his oft-cited remark that "all models are wrong, but some are useful".

In 2016, P.J. Bickel and K.A. Doksum published the second volume of their book on mathematical statistics. The volume includes the quote from Box's Presidential Address, given above. It states that the quote is the best formulation of the "guiding principle of modern statistics".

Additionally, in 2011, a workshop on model selection was held in The Netherlands. The name of the workshop was "All models are wrong...".

Historical antecedents

Although the aphorism seems to have originated with George Box, the underlying concept goes back decades, perhaps centuries. Some exemplifications of that are given below.

In 1960, Georg Rasch said the following.

… no models are [true]—not even the Newtonian laws. When you construct a model you leave out all the details which you, with the knowledge at your disposal, consider inessential…. Models should not be true, but it is important that they are applicable, and whether they are applicable for any given purpose must of course be investigated. This also means that a model is never accepted finally, only on trial.

— Rasch, G. (1960), Probabilistic Models for Some Intelligence and Attainment Tests, Copenhagen: Danmarks Paedagogiske Institut, pp. 37–38; republished in 1980 by University of Chicago Press

In 1947, the mathematician John von Neumann said that "truth … is much too complicated to allow anything but approximations".

In 1942, the French philosopher-poet Paul Valéry said the following.

Ce qui est simple est toujours faux. Ce qui ne l’est pas est inutilisable.	What is simple is always wrong. What is not is unusable.
—Valéry, Paul (1942), Mauvaises pensées et autres, Paris: Éditions Gallimard

In 1939, the founder of statistical process control, Walter Shewhart, said the following.

… no model can ever be theoretically attainable that will completely and uniquely characterize the indefinitely expansible concept of a state of statistical control. What is perhaps even more important, on the basis of a finite portion of the sequence [X₁, X₂, X₃, …]—and we can never have more than a finite portion—we can not reasonably hope to construct a model that will represent exactly any specific characteristic of a particular state of control even though such a state actually exists. Here the situation is much like that in physical science where we find a model of a molecule; any model is always an incomplete though useful picture of the conceived physical thing called a molecule.

— Shewhart, W. A. (1939), Statistical Method From the Viewpoint of Quality Control, U.S. Department of Agriculture, p. 19

In 1923, a related idea was articulated by the artist Pablo Picasso.

We all know that art is not truth. Art is a lie that makes us realize truth, at least the truth that is given us to understand. The artist must know the manner whereby to convince others of the truthfulness of his lies.

— Picasso, Pablo (1923), "Picasso speaks", The Arts, 3: 315–326; reprinted in Barr, Alfred H., Jr. (1939), Picasso: Forty Years of his Art (PDF), Museum of Modern Art, pp. 9–12