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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 ...
The classic example of experimenter bias is that of "Clever Hans", an Orlov Trotter horse claimed by his owner von Osten to be able to do arithmetic and other tasks. As a result of the large public interest in Clever Hans, philosopher and psychologist Carl Stumpf , along with his assistant Oskar Pfungst , investigated these claims.
Another methodological aspect is the avoidance of bias, which can involve cognitive bias, cultural bias, or sampling bias. Methods for avoiding or overcoming such biases include random sampling and double-blind trials. [3] However, objectivity in measurement can be unobtainable in certain circumstances.
Overconfidence effect, a tendency to have excessive confidence in one's own answers to questions. For example, for certain types of questions, answers that people rate as "99% certain" turn out to be wrong 40% of the time. [5] [43] [44] [45] Planning fallacy, the tendency for people to underestimate the time it will take them to complete a ...
Social science research is particularly prone to observer bias, so it is important in these fields to properly blind the researchers. In some cases, while blind experiments would be useful, they are impractical or unethical. Blinded data analysis can reduce bias, but is rarely used in social science research. [39]
Participants may remember the correct answers or may be conditioned to know that they are being tested. Repeatedly taking (the same or similar) intelligence tests usually leads to score gains, but instead of concluding that the underlying skills have changed for good, this threat to Internal Validity provides a good rival hypothesis.
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Some scholars classify cherry-picking as a fallacy of selective attention, the most common example of which is the confirmation bias. [3] Cherry picking can refer to the selection of data or data sets so a study or survey will give desired, predictable results which may be misleading or even completely contrary to reality.