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Although the reality of most of these biases is confirmed by reproducible research, [2] [3] there are often controversies about how to classify these biases or how to explain them. [4] Several theoretical causes are known for some cognitive biases , which provides a classification of biases by their common generative mechanism (such as noisy ...
Arguably, this is the most common type of scientific misconduct. Sometimes it is difficult to guess whether authors intentionally ignored a highly relevant cite or lacked knowledge of the prior work. Discovery credit can also be inadvertently reassigned from the original discoverer to a better-known researcher.
The University of Cambridge also investigated his research as a postdoctoral scholar at the Gurdon Institute from where he published several research papers on DNA damage. Two journals, Science and Nature retracted one article each, written with his mentor Stephen Jackson , published in 2010 and 2013 respectively, simultaneously on 11 April ...
Other research groups were not able to replicate the results and suggested that impurities in the material led to spurious effects mimicking phenomena associated with superconductivity. Copper(I) sulfide , a compound produced in the synthesis process, turned out to be a close match for the claimed properties of LK-99, and pure samples of LK-99 ...
Bias should be accounted for at every step of the data collection process, beginning with clearly defined research parameters and consideration of the team who will be conducting the research. [2] Observer bias may be reduced by implementing a blind or double-blind technique. Avoidance of p-hacking is essential to the process of accurate data ...
To promote a neutral (useless) product, a company must find or conduct, for example, 40 studies with a confidence level of 95%. If the product is useless, this would produce one study showing the product was beneficial, one study showing it was harmful, and thirty-eight inconclusive studies (38 is 95% of 40).
For example, confirmation bias produces systematic errors in scientific research based on inductive reasoning (the gradual accumulation of supportive evidence). Similarly, a police detective may identify a suspect early in an investigation, but then may only seek confirming rather than disconfirming evidence.
In statistical hypothesis testing, a type I error, or a false positive, is the rejection of the null hypothesis when it is actually true. A type II error, or a false negative, is the failure to reject a null hypothesis that is actually false. [1] Type I error: an innocent person may be convicted. Type II error: a guilty person may be not convicted.