Bias - "Any systematic error in a study that that can lead to conclusions that are different from the truth."

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Recall Bias - "Type of bias that occurs in epidemiological studies when self-reported, historical information is inaccurate. For example, in a case-control study cases may over-report past exposure, especially if it is widely known to be associated with the disease under study."

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Selection Bias - "Type of bias that occurs from the manner in which subjects are selected for a study. When the sample of subjects is not representative of the target population the results of the study may not be valid. For example in a case-control study of smoking and lung cancer, the association between the two will tend to be weaker if the controls are selected from a hospital population (because smoking is associated with many other diseases resulting in hospitalisation) than if controls are selected from the general population"

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Self-Serving Bias - "We attribute successes and positive outcomes to our doing, basking in our own glory when things go right; but, when we face failure and negative outcomes, we tend to attribute these events to other people or contextual factors outside ourselves."

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The Curse of Knowledge - "Similar in ways to the Availability Heuristic (Tversky & Kahneman, 1974) and to some extent, The False Consensus Effect, once you (truly) understand a new piece of information, that piece of information is now available to you and often becomes seemingly obvious. It might be easy to forget that there was ever a time you didn’t know this information and so, you assume that others, like yourself, also know this information: the Curse of Knowledge. However, it is often an unfair assumption that others share the same knowledge."

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Negativity Bias - "We like to win, but we hate to lose even more. So, when we make a decision, we generally think in terms of outcomes – either positive or negative. The bias comes into play when we irrationally weigh the potential for a negative outcome as more important than that of the positive outcome."

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The Backfire Effect - "The Backfire Effect may work based on the same foundation as Declinism, in that we do not like change. It is also similar to Negativity Bias, in that we wish to avoid losing and other negative outcomes – in this case, one’s idea is being challenged or rejected (i.e. perceived as being made out to be ‘wrong’) and thus, they may hold on tighter to the idea than they had before. However, there are caveats to the Backfire Effect – for example, we also tend to abandon a belief if there's enough evidence against it with regard to specific facts."

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The Fundamental Attribution Error - "The Fundamental Attribution Error is similar to the Self-Serving Bias, in that we look for contextual excuses for our failures, but generally blame other people or their characteristics for their failures"

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In-Group Bias - "In-Group Bias refers to the unfair favouring of someone from one’s own group. You might think that you’re unbiased, impartial and fair, but we all succumb to this bias, having evolved to be this way. That is, from an evolutionary perspective, this bias can be considered an advantage – favouring and protecting those similar to you, particularly with respect to kinship and the promotion of one’s own line."

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The Forer Effect - "Specifically, the Forer Effect refers to the tendency for people to accept vague and general personality descriptions as uniquely applicable to themselves without realizing that the same description could be applied to just about everyone else (Forer, 1949). For example, when people read their horoscope, even vague, general information can seem like it’s advising something relevant and specific to them."

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Confirmation Bias - "Occurs when the person performing the data analysis wants to prove a predetermined assumption. They then keep looking in the data until this assumption can be proven. E.g. by intentionally excluding particular variables from the analysis. This often occurs when data analysts are briefed in advance to support a particular conclusion.
It is therefore advisable to not doggedly set out to prove a predefined conclusion, but rather to test presumed hypotheses in a targeted way."

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Underfitting - "Underfitting means when a model gives an oversimplistic picture of reality. Overfitting is the opposite: i.e. when the model is overcomplicated. Overfitting risks causing a certain assumption to be treated as the truth whereas in practice it is actually not the case."

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Confounding Variabelen - "Failing to allow for confounding variables can result in assuming there is a cause-effect relationship between two variables when there is in fact another variable behind the phenomenon. Bear in mind that a correlation is not the same thing as cause-effect."

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Observer Bias - "Observer bias happens when the researcher subconsciously projects his/her expectations onto the research. It can come in many forms, such as (unintentionally) influencing participants (during interviews and surveys) or doing some serious cherry picking (focusing on the statistics that support our hypothesis rather than those that don’t.)"

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Survivorship Bias - "Survivorship bias is a statistical bias type in which the researcher focuses only on that part of the data set that already went through some kind of pre-selection process – and missing those data-points, that fell off during this process (because they are not visible anymore)." E.G - A website's success stories won't include customers who aren't satisfied.

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Omitted Variable Bias - "Omitted Variable Bias occurs when you are leaving out one or more important variables from your model."

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Cause-Effect Bias - "Our brain is wired to see causation everywhere that correlation shows up.
Cause-effect bias is usually not mentioned as a classic statistical bias, but I wanted to include it on this list as many decision makers (business/marketing managers) are not aware of that. Even those who are aware of it (including me), have to remind themselves from time to time: correlation does not imply causation."

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Conformity Bias - "If an individual feels the majority of the group are leaning towards/away from a certain candidate, they will tend to go along with the group thinks rather than voice their own opinions."

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The Halo Effect - "Halo is when we see one great thing about a person and we let the halo glow of that significant thing affect our opinions of everything else about that person."

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The Horns Effect - "The Horns effect is when we see one bad thing about a person and we let it cloud our opinions of their other attributes."

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I'd like to add my own as I can't find a bias for this readily.

 

The alternative bias - Giving extra weight and belief in views that contradict the mainstream simply for it being different.