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A 1996 study claiming a connection in men [399] has not been verified by subsequent studies, including a 2013 study that found no correlation. [400] The menstrual cycles of women who live together do not tend to synchronize. A 1971 study made this claim, but subsequent research has not supported it. [401] [402]
Common source bias, the tendency to combine or compare research studies from the same source, or from sources that use the same methodologies or data. [13] Conservatism bias, the tendency to insufficiently revise one's belief when presented with new evidence. [5] [14] [15]
Research has shown that science teachers have a wide repertoire to deal with misconceptions and report a variety of ways to respond to students' alternative conceptions, e.g., attempting to induce a cognitive conflict using analogies, requesting an elaboration of the conception, referencing specific flaws in reasoning, or offering a parallel ...
Better-powered studies refer to large studies that deliver definitive results or test major concepts and lead to low-bias meta-analysis. Enhanced research standards such as the pre-registration of protocols, the registration of data collections, and adherence to established protocols are other techniques.
The history of scientific method considers changes in the methodology of scientific inquiry, not the history of science itself. The development of rules for scientific reasoning has not been straightforward; scientific method has been the subject of intense and recurring debate throughout the history of science, and eminent natural philosophers and scientists have argued for the primacy of ...
The term "curse of knowledge" was coined in a 1989 Journal of Political Economy article by economists Colin Camerer, George Loewenstein, and Martin Weber.The aim of their research was to counter the "conventional assumptions in such (economic) analyses of asymmetric information in that better-informed agents can accurately anticipate the judgement of less-informed agents".
The deductive argument is called an explanation, its premisses are called the explanans (L: explaining) and the conclusion is called the explanandum (L: to be explained). Depending on a number of additional qualifications, an explanation may be ranked on a scale from potential to true. Not all explanations in science are of the D-N type, however.
Statistical assumptions can be put into two classes, depending upon which approach to inference is used. Model-based assumptions. These include the following three types: Distributional assumptions. Where a statistical model involves terms relating to random errors, assumptions may be made about the probability distribution of these errors. [5]