8/26/2023 0 Comments Data dredging/p-hackingThere are two types of Fact Findings in any analysis that ultimately assists in DSS i.e., 1) known facts 2) unknown facts. On occasions, it shows more details about something than it contains. These sometimes bypass Data Mining techniques and come up with immature conclusions. In other words, Data Dredging, Snooping, p-hacking, and Fishing share the results which require more investigation. At UZH the Center for Reproducible Science has been founded in 2018 with the objective to raise awareness regarding good scientific practice and to close gaps in the education to this effect.Data Dredging, Snooping and Fishing all refer to the same behavior of data analysis BUT without proper hypothesis and relationship among datasets.ĭata mining finds results based on the correlation of data in large data sets, but Data Dredging, Snooping, p-hacking, and Fishing find results based on chance methodology. Registration of study protocols before data collection, a well-proven measure in clinical research, see Kaplan and Irvin, 2015 on its consequences on the bias in literature.Īn entire series of articles regarding suggestions for improvement of scientific practice has been published in 2014 in The Lancet, Research: increasing value, reducing waste.Blinded data analysis, for example suggested by McCoun und Perlmutter inspired by conventions particle physics and cosmology.on genome-wide association studies or Kimmelman et al. Separating exploratory and confirmatory research clearly, see for example Kraft et al.How to overcome such prevalent reproducibility issues: There are several promising approaches on If you are a statistics amateur and want to understand the problem behind too many false positive results watch this Video by The Economist. In combination with low power this leads to serious problems, see for example Button et al 2016 in Nature. These practices contribute on the whole to a bias in the literature through too many published false positive and too few published true negative results. Only statistically significant results, non-significant ones end up in the To experiment until a significant result turns up Results as the ones that were searched for in the first place The Results are Known): describing in hindsight statistically significant But it appears that science in general has room for improvement concerning reproducibility, see for example Baker 2016 in Nature. These were much discussed in the media because they contain to a varying degree elements of fraud such cases are in reality rather rare. Impressive examples of misuse of data and of the massive damage this can inflict on science and society are the examples of Andrew Wakefield, Diedrik Stapel or Brian Wansink. That come back with significant results.” This isĭone by performing many statistical tests on the data and only reporting those So, what is p-hacking? Searching for p-hacking on Wikipedia leads to the article on data dredging that starts like this:ĭata snooping, data butchery, and p-hacking) is the misuse of data analysis toįind patterns in data that can be presented as statistically significant, thusĭramatically increasing and understating the risk of false positives.
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