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Use of hospital records rather than patient experience can also help to avoid recall bias. [8] Standardising sampling methods can help to avoid needing recall information in the first place. [9] Often, recall bias is difficult to avoid, and many studies change experiment design to avoid recalling information. [9]
Differences in perceptions of sexual interest between men and women may be exploited by both genders. Men may present themselves as more emotionally invested in a woman than they actually are in order to gain sexual access; 71% of men report engaging in this form of manipulation and 97% of women report having experienced this form of manipulation. [7]
Selection bias is the bias introduced by the selection of individuals, groups, or data for analysis in such a way that proper randomization is not achieved, thereby failing to ensure that the sample obtained is representative of the population intended to be analyzed. [1]
Algorithmic bias describes systematic and repeatable errors in a computer system that create "unfair" outcomes, such as "privileging" one category over another in ways different from the intended function of the algorithm.
Because they cause systematic errors, cognitive biases cannot be compensated for using a wisdom of the crowd technique of averaging answers from several people. [44] Debiasing is the reduction of biases in judgment and decision-making through incentives, nudges, and training.
As certain diagnoses become associated with behavior problems or intellectual disability, parents try to prevent their children from being stigmatized with those diagnoses, introducing further bias. Studies carefully selected from whole populations are showing that many conditions are much more common and usually much milder than formerly believed.
Additionally, there are many different types of attribution biases, such as the ultimate attribution error, fundamental attribution error, actor-observer bias, and hostile attribution bias. Each of these biases describes a specific tendency that people exhibit when reasoning about the cause of different behaviors. [3]
If the users know the amount of the systematic error, they may decide to adjust for it manually rather than having the instrument expensively adjusted to eliminate the error: e.g. in the above example they might manually reduce all the values read by about 4.8%.