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Common Biases Chapter 2

Common Biases Chapter 2. 13 Specific Biases bases upon availability heuristics. Bias 1: Ease of Recall. Vividness & recency Problem 3 Tobacco - 435,000 Poor diet – 400,000 Motor vehicle – 43,000 Firearms – 29,000 Illicit drugs – 17,000. Bias 1: Reason.

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Common Biases Chapter 2

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  1. Common Biases Chapter 2 13 Specific Biases bases upon availability heuristics

  2. Bias 1: Ease of Recall • Vividness & recency • Problem 3 • Tobacco - 435,000 • Poor diet – 400,000 • Motor vehicle – 43,000 • Firearms – 29,000 • Illicit drugs – 17,000

  3. Bias 1: Reason • Since there is some much press about drug and firearm death we assume a large amount of deaths per year.

  4. Bias 2: Retrivevability • Based upon memory structures • Answer 4b would include both ing words and any word with the letter n in the seven place, therefore 4b should the same or even slightly more • The human mind “sees” many words ending in ing you automatically assume 4a to be higher

  5. Bias 3: Presumed Associations • Chapman & Chapman (as cited by Bazerman, 2006) have noted that when the probability of two events co-occurring is judged by the availability of perceived co-occurring instances in our minds, we usually assign an inappropriately high probability that the two events will co occur again (p. 21).

  6. Bias 3 • Examples?

  7. Summary • Ease of recall, retrievability, and presumed associations indicate the misuse of availability heuristic • A lifetime of experience in general allow us to recall more likely events and frequent events

  8. Bias 4: Insensitivity to Base Rates • Problem 5? • Judgmental biases of this type frequently occur when individuals cognitively ask the wrong question • Peter Drucker (2005) the greatest source of mistakes in top management is to ask the same questions most people ask (n.p.) • “What needs to be done?” should be your question

  9. Bias 5: Insensitivity to Sample Size • Problem 6? • The small hospital! • Sampling theory, it is easier to 6 heads with 10 flips of a coin than 6,000 heads with 10,000 flips of a coin • Sizable sample much better in marketing research, but 4 out of 5 dentists surveyed recommend sugarless gum!

  10. Bias 6: Misconceptions of Chance • Problem 7? • Is the performance of the sixth stock related to the first five? • The hot hand belief (pp. 24) • The “law of small numbers” (pp. 25)

  11. Bias 7: Regression to the Mean • Problem 8? • Read the two examples on page 27; Flight instructors comments and management comments.

  12. Bias 8: The Conjunction Fallacy • Problem 9? • Look at your rankings of C,H,F • Did you place H before F? • Simple Statistics demonstrate that a conjunction cannot be more probable that any one of its descriptors, but will usually judged more probable than a single component descriptors • Most “feel” the conjunction is more representative than the component

  13. Representativeness Heuristic • Experience has taught us that the likelihood of a specific occurrence is related to the likelihood of a group of occurrences which that specific occurrence represents • Insensitivity to base rates, insensitivity to sample size, misconceptions of chance, regression to the mean, and the conjunction fallacy

  14. Lets go beyond availability and representativeness • Problem 9? • Irrelevant anchors • Anchors gives some info however incorrect it may be & there is probably some commonalities, right? • Roulette wheel & African countries in the UN

  15. Bias Conjunctive & Disjunctive Events Bias • Problem 10? • BAC • C-52%, A-50%, & B-48% • C & B are complements of each other • According to Tversky & Kahneman (as cited by Bazerman, 2006) “even when the likelihood of failure in each component is slight, the probability of an overall failure can be high if many components are involved” (p. 32).

  16. Overconfidence • Problem 12? • Overconfident when responding to questions of moderate to extreme difficulty • Imperfectly estimate their own performance?

  17. Bias 12: The Confirmation Trap • 2-4-6 rule – It is easier to find “see” the information backing up a decision than the bad information. • Yes person & Devils advocate consulting firms. “Our desire to confirm our initial ideas is so strong that we will pay people to back us up!” (Bazerman, 2006, p. 36). • “A willingness to attempt to falsify hypotheses, and thus to test those institutive ideas that so often carry the feeling of certitude” (Watson, 1960, pp. 139)

  18. Bias 13: Hindsight and the curse of knowledge • The hindsight bias (Fischhoff, 1975) • Curse of Knowledge

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