![]() ![]() There are several terms describing the act of data dredging. “When a large number of associations can be looked at in a dataset where only a few real associations exist, a P value of 0.05 is compatible with the large majority of findings still being false positives.” Data dredging results in false positive results. Data dredging, according to Wikipedia, is the inappropriate (sometimes deliberately so) use of data mining to uncover misleading relationships in data. Manufacturing: creating entire data sets de novo, … ĭata dredging is looking for too many possible associations in a dataset to see of any of them are statistically significant. ![]() Fudging: creating data points to augment incomplete data sets ….Slanting: … selecting certain trends in the data, … discarding others which do not fit ….Smoothing: discarding data points too far removed from expected … values.Extrapolating: … predicting future trends based on unsupported assumptions ….975 if viewed as one-sided) we would have observed a smaller value of M than we did. Massaging: … extensive transformations or other maneuvers to make inconclusive data appear … conclusive For example, there is good evidence a (the lower estimation limit) because if Improper data use undermines the ethos of science and the corresponding misleading results can misguide and distort the production of knowledge. ![]()
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