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Conjunction Fallacy

Also Known As: Linda problem conjunction error
Statistical Error ID: conjunction_fallacy

Definition

The conjunction fallacy occurs when people judge the probability of two events occurring together (a conjunction) as more likely than the probability of either event occurring alone. This violates a basic axiom of probability theory: P(A and B) can never exceed P(A) or P(B). The fallacy is driven by representativeness: when the conjunction creates a more coherent, plausible-sounding narrative, it feels more probable.

Examples

Linda is 31, outspoken, and majored in philosophy. She was active in anti-nuclear demonstrations. People rate 'Linda is a bank teller and active in the feminist movement' as MORE probable than 'Linda is a bank teller,' even though the conjunction must be less probable by the laws of probability.

Voters are told that a candidate is a former military officer who speaks frequently about national security. When asked to rank likelihoods, most say it is more probable that 'he will cut social programs AND increase defense spending' than simply 'he will increase defense spending,' even though the conjunction cannot exceed the probability of either component alone.

A news headline describes a tech entrepreneur as young, rebellious, and college-dropout. Readers rate 'She founded a startup AND dropped out to pursue it' as more likely than simply 'She dropped out of college,' because the added detail feels narratively coherent despite making the scenario statistically less probable.

Verification Steps
Verification Steps
Binary yes/no questions that an AI must answer to detect a reasoning pattern in a text.
Each of the 452 aspects has verification steps — simple yes/no questions designed to systematically detect whether a pattern appears in a text. For ad hominem: "Does the argument attack a person rather than their claim?" For false dichotomy: "Are only two options presented when more exist?" This ensures consistent, reproducible analysis.

Binary (yes/no) questions an LLM must answer to identify this aspect:

  1. 1

    Is the probability of a specific, detailed scenario being rated as higher than a more general one?

    Type: binary
  2. 2

    Does the more detailed scenario add conditions that should reduce probability?

    Type: binary
  3. 3

    Is the representativeness or narrative plausibility of a scenario being confused with its probability?

    Type: binary
  4. 4

    Does the probability assessment violate the conjunction rule?

    Type: binary
Deep Dive
The expandable detail section on each aspect page with examples, psychology, and counter-strategies.
The Deep Dive section provides in-depth information about each aspect: a real-world example showing the pattern in action, an explanation of why it works psychologically, practical advice on how to counter it, alternative names, and links to related aspects.

Hierarchical Context