Exercise 2-7: Regression artefact: Lord’s paradox

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Presentation transcript:

Exercise 2-7: Regression artefact: Lord’s paradox

Recapitulation I: Memory Judgments Judging of stability and change: Assessment of former attitudes is influenced by actual attitudes. Biased assessments of previous states (capabilities, pain) in order to explain «effect» of treatments. Hindsight bias: Biased retrospective evaluation of previous knowledge: «I knew it all along!»

Recapitulation II: Memory Judgments Basic Mechanism: Anchoring and adjustment: Actual state serves as an anchor that is adjusted due to subjective theories. Open-mindedness as an important aspect of critical thinking: Taking different perspectives.

Recapitulation III: Retrospective evaluation of episodes Central Result: A negative event of greater duration is preferred to a shorter negative event that is part of the longer event. Experiences and memory of experiences can differ. Snapshot model.

Recapitulation IV: Heuristics and Biases Program Availability Heuristic: The frequency of events is judged due to the easiness of how particluar instances can be generated (or come to mind). Examples: Memory and availability: Famous people Death rates. Influence of imagination.

Availability heuristic: Personal experience and exampels Ex. 4-6: Influence of personal experiences and examples. Central lession to be learned: Beware of arguments based on examples.

Probability Judgments: Representativeness Heuristic I Functioning: Assessment of the frequency of events according to similarity. Example: Evaluation of the probability of random sequences

Probability Judgments: Representativeness Heuristic II Example: Linda-Problem: Linda is 31 years old, single, outspoken and very bright. She majored in philosophy. As a student, she was deeply concerned with issues of discrimination and social justice, and also participated in anti-nuclear demonstrations. Ranking Statement (5.2) Linda is a teacher in elementary school. (3.3) Linda works in a bookstore and takes Yoga classes. (2.1) Linda is active in the feminist movement. (F) (3.1) Linda is a psychiatric social worker. (5.4) Linda is a member of the League of Women Voters. (6.2) Linda is a bank teller. (B) (6.4) Linda is an insurance salesperson. (4.1) Linda is a bank teller and is active in the feminist movement. (BF)

Probability Judgments: Representativeness Heuristic III Example: Political predictions: Rank Statement (1.5) Reagan will cut federal support to local government. (B) (3.3) Reagan will provide support for unwed mothers. (A) (2.7) Reagan will increase the defense budget by less than 5%. (2.9) Reagan will provide federal support for unwed mothers and cut federal support for local government. (AB)

Probability Judgments: Representativeness Heuristic IV Conclusion (Basic lession): Beware of detailed internally coherent and plausible scenarios (those concerning the future as well as those concerning the past). More detailed scenarios appear as more plausible. However more detailed scenarios are less probable since each added detail reduces the probability of the scenario.

Probability Judgments: Probability Matching Basic phenomenon: Peoples’ answers reflect probabilities 70% 30%

Probability Judgments: Probability Matching Non-optimality of PM: With PM 58% correct, with optimal strategy: 70% correct. Outcome Participant’s prediction  »Red light« »Green light« Red light 0.49 0.21 0.70 Green light 0.09 0.30

Probability Judgments: Probability Matching (PM) Humans and animals: Rats and students Animals: birds Animals: ducks Individual differences: Intelligence and PM Gender differences

Probability Judgments: Probability Matching Explanation of PM: Greed as a possible explanation: Trying to get reward from both sources. Rationality and PM.

Probability Judgments: Conditional probabilities (CP) Conception CP and stochastic independence of A and B:

Probability Judgments: Conditional probabilities (CP) Asymmetry of CPs :

Probability Judgments: Conditional probabilities (CP) CP and Causal Reasoning: Preference for causal to diagnostic reasoning contradicts the principle: Example 4-14:

Probability Judgments: Conditional probabilities (CP) Non-monotonic CP: New facts can completely reorder probabilities: Yet and

Probability Judgments: Conditional probabilities (CP) Non-monotonic CP: Example Simpson paradox: yet, and

Probability Judgments: Base rate neglect Ignoring base rates What is base rate information? Example: Base rate neglect Causal base rates