Search results
Results from the WOW.Com Content Network
Recall bias is a type of measurement bias, and can be a methodological issue in research involving interviews or questionnaires. In this case, it could lead to misclassification of various types of exposure . [ 2 ]
The effect(s) of such misclassification can vary from an overestimation to an underestimation of the true value. [4] Statisticians have developed methods to adjust for this type of bias, which may assist somewhat in compensating for this problem when known and when it is quantifiable. [5]
In psychology and cognitive science, a memory bias is a cognitive bias that either enhances or impairs the recall of a memory (either the chances that the memory will be recalled at all, or the amount of time it takes for it to be recalled, or both), or that alters the content of a reported memory. There are many types of memory bias, including:
Many researchers have attempted to identify the psychological process which creates the availability heuristic. Tversky and Kahneman argue that the number of examples recalled from memory is used to infer the frequency with which such instances occur. In an experiment to test this explanation, participants listened to lists of names containing ei
The misinformation effect also appears to stem from memory impairment, meaning that post-event misinformation makes it harder for people to remember the event. [7] The misinformation reflects two of the cardinal sins of memory: suggestibility , the influence of others' expectations on our memory; and misattribution , information attributed to ...
Get AOL Mail for FREE! Manage your email like never before with travel, photo & document views. Personalize your inbox with themes & tabs. You've Got Mail!
An F-score is a combination of the precision and the recall, providing a single score. There is a one-parameter family of statistics, with parameter β, which determines the relative weights of precision and recall. The traditional or balanced F-score is the harmonic mean of precision and recall:
This might be done in order to achieve "desireable", best performances for each class (potentially measured as precision and recall in each class). Finding the best multi-class classification performance or the best tradeoff between precision and recall is, however, inherently a multi-objective optimization problem.