Editing Confirmation bias (section)

=== Illusory association between events ===
{{Main|Illusory correlation}}
Illusory correlation is the tendency to see non-existent correlations in a set of data.<ref name=fine>{{Harvnb |Fine|2006|pp=66–70}}</ref> This tendency was first demonstrated in a series of experiments in the late 1960s.<ref name="plous164">{{Harvnb |Plous|1993|pp=164–166}}</ref> In one experiment, participants read a set of psychiatric case studies, including responses to the [[Rorschach inkblot test]]. The participants 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 fictional case studies had been constructed so that the homosexual men were no more likely to report this imagery or, in one version of the experiment, were less likely to report it than heterosexual men.<ref name=fine /> In a survey, a group of experienced psychoanalysts reported the same set of illusory associations with homosexuality.<ref name=fine /><ref name=plous164 />

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.<ref>{{Citation |last1=Redelmeir |first1=D.A. |first2=Amos |last2=Tversky |year=1996 |title=On the belief that arthritis pain is related to the weather |journal=Proceedings of the National Academy of Sciences |volume=93 |pages=2895–2896 |doi=10.1073/pnas.93.7.2895 |pmid=8610138 |issue=7|bibcode=1996PNAS...93.2895R |pmc=39730 |doi-access=free }} via {{Harvnb|Kunda|1999|p=127}}</ref>
{| class="wikitable" style="width:250px;text-align:center;margin: 1em auto 1em auto"
|+ Example
|-
! Days !! Rain !! No rain
|-

! Arthritis
| 14 || 6
|-
! No arthritis
| 7 || 2
|}

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.<ref name="kunda127">{{Harvnb|Kunda|1999|pp=127–130}}</ref> 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 or good weather).<ref name="plous162">{{Harvnb|Plous|1993|pp=162–164}}</ref> This parallels the reliance on positive tests in hypothesis testing.<ref name="kunda127" /> 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.<ref name="kunda127" />