Bias Time (2 of 9)


[See how the metro entrance folds; Valencia of course]

Yes, it’s bias time again. The second of the series of biases that you, yes you, have. [Part one here] Even if you are aware of these, and even if you consciously try to correct for them to be, heh, ‘objective’, as in what e.g. auditors pursue, you will fail.

Biases in probability and belief

  • Ambiguity effect – the tendency to avoid options for which missing information makes the probability seem “unknown.”
  • Anchoring effect – the tendency to rely too heavily, or “anchor,” on a past reference or on one trait or piece of information when making decisions (also called “insufficient adjustment”).
  • Attentional bias – the tendency to neglect relevant data when making judgments of a correlation or association.
  • Authority bias – the tendency to value an ambiguous stimulus (e.g., an art performance) according to the opinion of someone who is seen as an authority on the topic.
  • Availability heuristic – estimating what is more likely by what is more available in memory, which is biased toward vivid, unusual, or emotionally charged examples.
  • Availability cascade – a self-reinforcing process in which a collective belief gains more and more plausibility through its increasing repetition in public discourse (or “repeat something long enough and it will become true”).
  • Belief bias – an effect where someone’s evaluation of the logical strength of an argument is biased by the believability of the conclusion.
  • Clustering illusion – the tendency to see patterns where actually none exist.
  • Capability bias – the tendency to believe that the closer average performance is to a target, the tighter the distribution of the data set.
  • Choice-supportive bias – The tendency to remember one’s choices as better than they actually were
  • Conjunction fallacy – the tendency to assume that specific conditions are more probable than general ones.
  • Disposition effect – the tendency to sell assets that have increased in value but hold assets that have decreased in value.
  • Gambler’s fallacy – the tendency to think that future probabilities are altered by past events, when in reality they are unchanged. Results from an erroneous conceptualization of the Law of large numbers. For example, “I’ve flipped heads with this coin five times consecutively, so the chance of tails coming out on the sixth flip is much greater than heads.”
  • Hawthorne effect – the tendency to perform or perceive differently when one knows they are being observed.
  • Hindsight bias – sometimes called the “I-knew-it-all-along” effect, the tendency to see past events as being predictable.
  • Illusory correlation – beliefs that inaccurately suppose a relationship between a certain type of action and an effect.
  • Last illusion – The belief that someone must know what is going on
  • Neglect of prior base rates effect – the tendency to neglect known odds when reevaluating odds in light of weak evidence.
  • Observer-expectancy effect – when a researcher expects a given result and therefore unconsciously manipulates an experiment or misinterprets data in order to find it (see also subject-expectancy effect).
  • Optimism bias – the tendency to be over-optimistic about the outcome of planned actions.
  • Ostrich effect – ignoring an obvious (negative) situation.
  • Overconfidence effect – excessive confidence in one’s own answers to questions. For example, for certain types of questions, answers that people rate as “99% certain” turn out to be wrong 40% of the time.
  • Positive outcome bias – the tendency to overestimate the probability of good things happening to them (see also wishful thinking, optimism bias, and valence effect).
  • Pareidolia – a vague and random stimulus (often an image or sound) is perceived as significant, e.g., seeing images of animals or faces in clouds, the man in the moon, and hearing hidden messages on records played in reverse.
  • Primacy effect – the tendency to weigh initial events more than subsequent events.
  • Recency effect – the tendency to weigh recent events more than earlier events (see also peak-end rule).
  • Disregard of regression toward the mean – the tendency to expect extreme performance to continue.
  • Selection bias – a distortion of evidence or data that arises from the way that the data are collected.
  • Stereotyping – expecting a member of a group to have certain characteristics without having actual information about that individual.
  • Subadditivity effect – the tendency to judge probability of the whole to be less than the probabilities of the parts.
  • Subjective validation – perception that something is true if a subject’s belief demands it to be true. Also assigns perceived connections between coincidences.
  • Survivorship bias – the tendency to concentrate on the people or things that “survived” some process and ignoring those that didn’t, or arguing that a strategy is effective given the winners, while ignoring the large number of losers.
  • Telescoping effect – the effect that recent events appear to have occurred more remotely and remote events appear to have occurred more recently.
  • Texas sharpshooter fallacy – the fallacy of selecting or adjusting a hypothesis after the data is collected, making it impossible to test the hypothesis fairly. Refers to the concept of firing shots at a barn door, drawing a circle around the best group, and declaring that to be the target.
  • Well travelled road effect – underestimation of the duration taken to traverse oft-traveled routes and over-estimate the duration taken to traverse less familiar routes.
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About maverisk

Maverisk Consultancy, IS Audit and Advisory services: Wikinomics meets governance and audit; otherwise, see my personal LinkedIn profile
This entry was posted in ERM, GRC, Information Risk Management, Sociological, psychological notes. Bookmark the permalink.

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