Biases in probability and belief
From Wikipedia
Many of these biases are often studied for how they affect business and economic decisions and how they affect experimental research.
• Ambiguity effect – the tendency to avoid options for which missing information makes the probability seem "unknown."[26]
• 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").
• Base rate neglect' or Base rate fallacy – the tendency to base judgments on specifics, ignoring general statistical information.[27]
• Belief bias – an effect where someone's evaluation of the logical strength of an argument is biased by the believability of the conclusion.[28]
• 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.
• Conjunction fallacy – the tendency to assume that specific conditions are more probable than general ones.[29]
• Forward Bias - the tendency to create models based on past data which are validated only against that past data.
• 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."
• Hindsight bias – sometimes called the "I-knew-it-all-along" effect, the tendency to see past events as being predictable.[30]
• Illusory correlation – inaccurately perceiving a relationship between two events, either because of prejudice or selective processing of information.[31]
• 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.[32]
• 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.[33][34]
• Positive outcome bias – the tendency of one to overestimate the probability of a favorable outcome coming to pass in a given situation (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.
• Pessimism bias – the tendency for some people, especially those suffering from depression, to overestimate the likelihood of negative things happening to them.
• Primacy effect – the tendency to weigh initial events more than subsequent events.[35]
• 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.
• 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.
• 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.
From Wikipedia
Many of these biases are often studied for how they affect business and economic decisions and how they affect experimental research.
• Ambiguity effect – the tendency to avoid options for which missing information makes the probability seem "unknown."[26]
• 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").
• Base rate neglect' or Base rate fallacy – the tendency to base judgments on specifics, ignoring general statistical information.[27]
• Belief bias – an effect where someone's evaluation of the logical strength of an argument is biased by the believability of the conclusion.[28]
• 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.
• Conjunction fallacy – the tendency to assume that specific conditions are more probable than general ones.[29]
• Forward Bias - the tendency to create models based on past data which are validated only against that past data.
• 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."
• Hindsight bias – sometimes called the "I-knew-it-all-along" effect, the tendency to see past events as being predictable.[30]
• Illusory correlation – inaccurately perceiving a relationship between two events, either because of prejudice or selective processing of information.[31]
• 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.[32]
• 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.[33][34]
• Positive outcome bias – the tendency of one to overestimate the probability of a favorable outcome coming to pass in a given situation (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.
• Pessimism bias – the tendency for some people, especially those suffering from depression, to overestimate the likelihood of negative things happening to them.
• Primacy effect – the tendency to weigh initial events more than subsequent events.[35]
• 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.
• 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.
• 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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