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Faith Quote by Thomas Gilovich

“With respect to the formation of erroneous beliefs, the implications of people’s difficulties in detecting covariation should be clear. By placing too much emphasis on positive instances, people will occasionally “detect” relationships that are not there. For many of the real-world phenomena that…” quote by Thomas Gilovich
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““With respect to the formation of erroneous beliefs, the implications of people’s difficulties in detecting covariation should be clear. By placing too much emphasis on positive instances, people will occasionally “detect” relationships that are not there. For many of the real-world phenomena that are of greatest interest, one is sure to encounter many positive instances even when there is no relationship at all between the two variables. Although there is surely no validity to the common belief that we are more likely to need something once we have thrown it away, examples of acute longing for a discarded possession may be easy to come by. By letting necessary evidence “slip by” as sufficient evidence, people establish an insufficient threshold of what constitutes adequate support for a belief, and they run the risk of believing things that are not true.””

Thomas Gilovich

About This Quote

Source Article: Judgment and Decision Making, 2010

People often overestimate relationships by focusing on positive examples, leading to false beliefs when evidence is insufficient.

In simple terms: Focusing on positives can cause false conclusions.

Key Takeaway

Check evidence before forming beliefs.

Themes

cognition bias evidence belief formation

Mood

skeptical analytical

Type

academic explanatory

When to use this quote

  • research design
  • media reporting
  • personal decision making
  • policy analysis

Key Concepts

covariation detection confirmation bias illusory correlation

Questions to Reflect On

  • How do you verify a relationship before accepting it?
  • What evidence would change your belief?
A Different Perspective

Overemphasis on positives may ignore contradictory data.

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