Heuristics are tools we use to simplify the decision-making process thus giving us efficiency but sometimes at the cost of accuracy. Heuristics results in many different forms of biases which are listed below.

Memory Biases

In retrospect, the degree to which an event could have been predicted is often overestimated.

Example: When you see that the sky is grey so you decide to bring an umbrella incase it rains. When it actually rains, you think to yourself that you knew it all along
An event may be judged more probable if it can be easily imagined.

Example: You’re going on a road trip and has to prepare for things that might go wrong. You easily imagine a flat tire and therefore think that there's a greater chance of getting a flat tire as opposed to something not as easily imagined such as getting a crack in the windshield from a pebble. When in reality the likelihood of these two events may be the same.
An event or class may appear more numerous or frequent if its instances are more easily recalled than other equally probable events.

Example: In a study about cancer, those who had cancer might search through their memory more thoroughly about their exposure to certain factors than those who did not have cancer. This may skew the results in the search of cancer causing factors.
An event may seem more frequent because of the effectiveness of the search strategy.

Example: In the english language are there more words that start with the letter r or has r as the third letter? Because it is easier to search for words that start with r than those that has r as the third letter, most may think that there are more words that start with r. In reality, there are actually more words with r as the third letter.
The likelihood of an event occurring may be judged by the degree of similarity with the class it is perceived to belong to.

Example: If you really liked the Titanic directed by James Cameron, you are more likely to watch future movies directed by him because you think that you will like those as well.
The inability to recall details of an event may lead to seemingly logical reconstructions that may be inaccurate.

Example: If someone asked you “did it rain on tuesday” vs “what was the weather like on tuesday”, you are more likely to recall that it rained on tuesday than in the first question.

Situation Biases

A decision making situation can be simplified by ignoring or significantly discounting the level of uncertainty.

Example: You decide to take your usual route to work but is surprised that there is extra traffic on the road today. But you knew in advance that there is a special event going on at one of the arenas along the way to your office.
Time pressure, information overload and other environmental factors can increase the perceived complexity of a test.

Example: A decision may seem harder to you if you only had 1 minute to make it as opposed to 1 hour even though it is the same decision that you have to make.
Often decision-makers commit to follow or escalate a previous unsatisfactory course of action. Increasing the commitment of resources to a decision even after the previous decision is known to have been incorrect.

Example: You own a really old car that always breaks down on you. You have already repaired the car 3 times for different reasons, but you car broke down again. You decide to fix the car again because you already put so much money into the car. This is not a good decision because the costs are already sunk, you will probably be better off if you just got a new car all together.
An alternative may be chosen only because it was used before.

Example: You buy crest toothpaste because that's the brand you’ve been loyal to every since high school despite the fact that there are other better quality and cheaper alternatives.
Often a consistent judgement strategy is not applied to an identical repetitive set of cases. Boredom, distraction, fatigue, and other general human factors may affect the consistency of certain results.

Example: A teacher marking assignments may not be 100% consistent in their marking because there are many assignments to go through. This is way often times they remark the first 10 papers so that they’re not too easy or too hard on the first few assignments that they mark.
The wrong decision rule may be used.

Example: When you are deciding on a post secondary education, you have to decide if you will enroll in the Universities which are the closest to you, or the ones that are the most acclaimed. You choose convenience over prestige, and this could be the wrong decision rule when it comes to selecting a University.

Statistical Biases

Base rate data tends to be ignored when other data are available.

Example: When you know that the probability of hailing a white or a yellow taxi cab is equally 50%, but the last two times you hailed a taxi, they were both yellow. This leads you to believe that it is more likely that the next taxi cab you hail will also be yellow. You choose to ignore the prior knowledge and focus on the specific information.
A sequence of random events can be mistaken for an essential characteristic of a process.

Example: A red win at a roulette table is very probable after a series of five black wins, and must be approaching certainty.
Probability is often overestimated in compound conjunctive problems.

Example: You’re at a grocery store and you witness your professor buying groceries. He buys an assortment of vegetables, tofu and fruits. You believe that it is more likely that Steve is a professor who is vegetarian, rather than Steve is a professor. You infer that a conjunction is more probable rather than one of its conjuncts.
Probability of two events occurring together can be overestimated if they have co-occurred in the past.

Example: Lucky charms basically works this way. If you got an A from an exam by wearing a particular shirt, you may wear that shirt again on another exam because you believe it to be a lucky shirt.
Probability is often underestimated in compound disjunctive problems.

Example: For a whole entire software program to fail, only one of the large number of key elements has to fail, not the entire program as a whole.
The size of a sample is often ignored in judging its predictive power.

Example: There are many scientific studies out there and many are being published daily. When looking at them we have to be skeptical of the results by looking at how big the sample size is. A group of 20 people is not sufficient to prove that eating grapes makes you happier.
A conjunction or subset is often judged more probable than its set.

Example:You believe that it is more likely that the Vancouver Canucks will score the first goal and lose the match, rather than the Canuck’s simply just losing the game all together.

Cognitive Biases

The perception of an apparently complete or logical data presentation can stop the research for omissions.

Example: You witness a robbery at your local corner store. You are presented with pictures of possible suspects, and you are asked to identify the robber. You are certain the robber must be included in this list of suspects, even though the robber’s picture may not be included.
A poor decision may lead to a good outcome, inducing a false feeling of control over the judgement situation.

Example: You pull an all-nighter to study for an exam, and you do very well on the exam the next day. You know it’s a bad idea to pull an all-nighter and study last minute but you did well on the exam, so you perceive this to be an effective study habit.
The probability of desired outcomes may be inaccurately assessed as being greater.

Example: You decide to eat a salad, and you feel as if you’re being much healthier and look better just after one healthy choice.
Often decision-makers seek confirmatory evidence and do not search for disconfirming information.

Example: I am a fan of the iphone, i will always notice the good characteristics about it and never take into account the negative aspects.
The ability to solve difficult or novel problems is often overestimated.

Example: You are intimidated by an onerous individual project in one of your classes. However, you are much more optimistic about the difficulty of a group project in the same class because you believe it’ll be easier completing a project collectively.
The more redundant and voluminous the data, the more confidence may be expressed in its accuracy and importance.

Example: Your coach continuously tells you that you are the greatest player on your softball team. You will end up overestimating the importance of this event, as it is repeated constantly to you.
Expectation of the nature of an event can bias what information is thought to be relevant.

Example: Certain information may be excluded during a decision making process because of a person’s education, profession or background. A business problem may be perceived differently by the marketing, finance or information technology departments, as specific aspects are thought to be more important for each department.
Often failure is associated with poor luck, and success with the abilities of the decision-maker.

Example: If you fail your driver’s license exam the first time, you attribute it to poor luck because your judicator wasn’t fair. However, you weren’t surprised when you passed it the second time because you think you’re a good driver and the first time was just bad luck.
Some aspects and outcomes of choice cannot be tested, leading to unrealistic confidence in judgement.

Example: The decision outcomes from a hiring process cannot be tested. You cannot compare the performance of the applicants who were successfully hired, to those who did not make the cut. Even if the applicants who were hired may be competent, you will not be able to test their performance against people who weren’t hired.

Adjustment Biases

Adjustments from an initial position are usually insufficient.

Example: You negotiate a good phone plan price with your service provider. The price that you end up with will usually be higher if the salesperson pitched you a more expensive plan in the beginning than if he straight up offered you a cheaper plan.
Often estimates are not revised appropriately on the receipt of new significant data.

Example: You are driving to an address that you have never been to before, you GPS takes you down a rugged path that does not look right but you decide to follow it anyways because you are undermining the new information that the path looks wrong and that you may be lost.
The establishment of a reference point or anchor can be a random or distorted act.

Example: Different people may view different traits differently. For example, your definition of lazy may be very different from another person’s definition of lazy. And therefore when evaluating a person, they may be deemed lazy by one person and not by another.
That events will tend to regress towards the mean on subsequent trial is often not allowed for in judgement.

Example: When you’re sick and go to the doctor, after going to the doctor you feel better so you think that the doctor did something special. When in fact, people tend to get better anyways when they are sick.

Presentation Biases

Events framed as either losses or gains may be evaluated differently.

Example: When you prefer to buy 75% lean beef instead of 25% fat beef.
Decision-makers are often unable to extrapolate a non-linear growth process.

Example: You think that travelling twice as far will take twice as long but sometimes the time it takes to travel longer distances is proportionally shorter because you’re driving on the highway as opposed to driving in the city.
The mode and mixture of presentation can influence the perceived value of data.

Example: You make take a face-to-face conversation more seriously and of higher priority than a phone even though the content of these conversations may be exactly the same.
The first or last item presented may be overweight in judgement.

Example: When watching many presentations in a row, the first and the last presentations are usually the most memorable.
The perceived variability of data can be affected by the scale of the data.

Example: It is easier for you to identify the difference in data is between 70 and 80 than 7 billion and 8 billion.

Copyright © Octothorpe Software Corporation 2017 · All Rights Reserved