I decided to compile a list of my favorite cognitive biases. If you don’t know what cognitive bias means, it refers to a mistaken or illogical judgment due to some unfairness in the context of the judgment. Being aware of these biases is so important, they affect our everyday social exchanges, careers, relationships, and much more. If you are working on things like product design or even machine learning where human behavior is involved, it’s important to consider cognitive biases.
Gambler’s fallacy: The tendency to think that future probabilities are altered by past events, when in reality they are unchanged. The fallacy arises 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.”
Rosy retrospection: The remembering of the past as having been better than it really was.
Whenever people talk about the “good old days” you should call them out with this bias. For example, how some people just love glamorizing America in the 50’s. Or, some people saying how Instagram / Twitter were so much better before the latest update.
Illusory truth effect: A tendency to believe that a statement is true if it is easier to process, or if it has been stated multiple times, regardless of its actual veracity. These are specific cases of truthiness.
Nicely done Donald Trump. Behold the power of the Illusory truth effect, it wins you elections.
IKEA effect: The tendency for people to place a disproportionately high value on objects that they partially assembled themselves, such as furniture from IKEA, regardless of the quality of the end result.
This one can be generalized. For example, you can coerce someone into doing something they wouldn’t normally do by convincing them that it was their idea. My dad tells me this is a classic Microsoft political move employees would pull on eachother.
Dunning-Kruger effect: The tendency for unskilled individuals to overestimate their own ability and the tendency for experts to underestimate their own ability.
Framing effect: Drawing different conclusions from the same information, depending on how that information is presented
Omission bias: The tendency to judge harmful actions as worse, or less moral, than equally harmful omissions (inactions).
Belief bias: An effect where someone’s evaluation of the logical strength of an argument is biased by the believability of the conclusion.
This one comes up way more commonly in life than you’d expect. Be on the lookout to consider believability (penalize more believable arguments!!) when making important decisions.
Bizarreness effect: Bizarre material is better remembered than common material.
Clustering illusion: The tendency to overestimate the importance of small runs, streaks, or clusters in large samples of random data (that is, seeing phantom patterns).
Confirmation bias: The tendency to search for, interpret, focus on and remember information in a way that confirms one’s preconceptions.
Yep, this is the one you were forced to learn in highschool. I down-ranked it on this list because it is likely everyone has already heard of confirmation bias.
Experimenter’s bias: The tendency for experimenters to believe, certify, and publish data that agree with their expectations for the outcome of an experiment, and to disbelieve, discard, or downgrade the corresponding weightings for data that appear to conflict with those expectations.
Courtesy bias: The tendency to give an opinion that is more socially correct than one’s true opinion, so as to avoid offending anyone.
I think everyone knows about this bias but nobody takes action on debiasing it. Don’t be scared to prod others to share their true feelings!
Continued influence effect: The tendency to believe previously learned misinformation even after it has been corrected. Misinformation can still influence inferences one generates after a correction has occurred.
Hindsight bias: Sometimes called the “I-knew-it-all-along” effect, the tendency to see past events as being predictable at the time those events happened.
Time-saving bias: Underestimations of the time that could be saved (or lost) when increasing (or decreasing) from a relatively low speed and overestimations of the time that could be saved (or lost) when increasing (or decreasing) from a relatively high speed.