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Another key example of observer bias is a 1963 study, "Psychology of the Scientist: V. Three Experiments in Experimenter Bias", [9] published by researchers Robert Rosenthal and Kermit L. Fode at the University of North Dakota. In this study, Rosenthal and Fode gave a group of twelve psychology students a total of sixty rats to run in some ...
Detection bias occurs when a phenomenon is more likely to be observed for a particular set of study subjects. For instance, the syndemic involving obesity and diabetes may mean doctors are more likely to look for diabetes in obese patients than in thinner patients, leading to an inflation in diabetes among obese patients because of skewed detection efforts.
Detection theory or signal detection theory is a means to measure the ability to differentiate between information-bearing patterns (called stimulus in living organisms, signal in machines) and random patterns that distract from the information (called noise, consisting of background stimuli and random activity of the detection machine and of the nervous system of the operator).
Observer-expectancy effect, a form of reactivity in which a researcher's cognitive bias causes them to unconsciously influence the participants of an experiment; Observer bias, a detection bias in research studies resulting for example from an observer's cognitive biases
Agent detection bias, the inclination to presume the purposeful intervention of a sentient or intelligent agent. Automation bias , the tendency to depend excessively on automated systems which can lead to erroneous automated information overriding correct decisions.
Lead time bias happens when survival time appears longer because diagnosis was done earlier (for instance, by screening), irrespective of whether the patient lived longer. Lead time is the duration of time between the detection of a disease (by screening or based on new experimental criteria) and its usual clinical presentation and diagnosis ...
[4] [5] It is important to note that when examining items for DIF, the groups must be matched on the measured attribute, otherwise this may result in inaccurate detection of DIF. In order to create a general understanding of DIF or measurement bias, consider the following example offered by Osterlind and Everson (2009). [6]
As an alternative view of deception and detection, truth-default theory was introduced by Timothy R. Levine. Levine is a Professor and Chair of Communication Studies at University of Alabama Birmingham. While experimenting with deception detection, Levine found that, even in high suspicion situations, truth-bias still occurred.