Belief and Reasoning Biases
Reasoning biases shape how we interpret evidence, defend beliefs, and decide what counts as proof.
01
Belief bias
Judging an argument by whether its conclusion seems believable.
02
Belief perseverance
Holding a belief after its support has been weakened.
03
Confirmation bias
Favoring evidence that supports existing beliefs.
04
Disconfirmation bias
Scrutinizing opposing evidence more harshly than supporting evidence.
05
Motivated reasoning
Reasoning toward the answer you want to be true.
06
Backfire effect
Sometimes correcting a belief can make it feel more entrenched.
07
Congruence bias
Testing only the possibility you already expect.
08
Selective perception
Noticing information that fits expectations.
09
Selective exposure
Choosing information sources that confirm existing views.
10
Semmelweis reflex
Rejecting new evidence because it challenges established norms.
11
Conservatism bias
Updating beliefs too slowly when new evidence arrives.
12
Bayesian conservatism
Underweighting new probability information.
13
Continued influence effect
Letting misinformation keep influencing you after correction.
14
Illusory truth effect
Repeated statements feel more true.
15
Availability bias
Using easily recalled information as if it were representative.
16
Narrative fallacy
Overvaluing a tidy story over messy reality.
17
Clustering illusion
Seeing patterns in random clusters.
18
Apophenia
Perceiving meaningful connections in unrelated things.
19
Pareidolia
Seeing recognizable patterns where none were intended.
20
Texas sharpshooter fallacy
Choosing data after the fact to fit a claim.
21
Pattern recognition bias
Overdetecting patterns because patterns feel useful.
22
Causal illusion
Seeing cause where there is only association.
23
Post hoc fallacy
Assuming that because B followed A, A caused B.
24
Non sequitur
Drawing a conclusion that is not supported by what came before.
25
Automation bias
Overtrusting automated output.
26
Automation complacency
Monitoring less carefully because a system usually works.
27
Algorithm aversion
Distrusting algorithms after seeing them make mistakes.
28
Algorithm appreciation
Overvaluing algorithmic advice because it seems objective.