Illusory correlation is the phenomenon of seeing the
relationship one expects in a set of data even when no such relationship exists. When people form false associations between membership in a statistical minority group and rare (typically negative) behaviors, this would be a common example of illusory correlation.
[1] This happens because the variables capture the attention simply because they are novel or deviant. This is one way
stereotypes form and endure. David Hamilton and Terrence Rose (1980) found that stereotypes can lead people to expect certain groups and traits to fit together, and they overestimate the frequency of when these correlations actually occur.
[2] People overestimate the core association between variables such as stereotyped groups and stereotypic behavior.
[3]
Illusory correlation is the tendency to see non-existent correlations in a set of data.
[41] This tendency was first demonstrated in a series of experiments in the late 1960s.
[42] In one experiment, subjects read a set of psychiatric case studies, including responses to the
Rorschach inkblot test. They reported that the homosexual men in the set were more likely to report seeing buttocks, anuses or sexually ambiguous figures in the inkblots. In fact the case studies were fictional and, in one version of the experiment, had been constructed so that the homosexual men were less likely to report this imagery.
[41] In a survey, a group of experienced psychoanalysts reported the same set of illusory associations with homosexuality.
[42][41] Another study recorded the symptoms experienced by arthritic patients, along with weather conditions over a 15-month period. Nearly all the patients reported that their pains were correlated with weather conditions, although the real correlation was zero.
[43]
This effect is a kind of biased interpretation, in that objectively neutral or unfavorable evidence is interpreted to support existing beliefs. It is also related to biases in hypothesis-testing behavior.
[44] In judging whether two events, such as illness and bad weather, are correlated,
people rely heavily on the number of positive-positive cases: in this example, instances of both pain and bad weather.
They pay relatively little attention to the other kinds of observation (of no pain and/or good weather).[45] This parallels the reliance on positive tests in hypothesis testing.
[44] It may also reflect selective recall, in that people may have a sense that two events are correlated because it is easier to recall times when they happened together.
[44]