![]() ![]() And please remember - being a full-time professor leaves only limited time to respond to comments.In our explainer on statistical significance and statistical testing, we introduced how you go about testing a hypothesis, and what can be legitimately inferred from the results of a statistical test. ![]() Comments - especially anonymous ones - with pseudo argumentations, abusive language or irrelevant links will not be posted. A scientist’s training is all about developing techniques to counteract this tendency, and so open practices are just another tool for achieving that purpose. ![]() Preregistration and open data can help protect yourself against your mind’s natural tendency to perceive patterns in noise. All this fussing about p-hacking glosses over the fact that the same analytic flexibility or data dredging can be applied to any inference, whether it is based on p-values, confidence intervals, Bayes factors, posterior probabilities, or simple summary statistics …Įverybody p-hacks if left to their own devices. Moreover, while null-hypothesis significance testing with p-values is still by far the most widespread way to make inferences about results, it is not the only way. The end result may very well be the same in that you continue abusing the data until a finding becomes significant, but I would bet that in most cases what matters to people is not the p-value but the result. However, the p-value is usually probably secondary to the act here. That may be something you predicted but didn’t find or could even just be some chance finding that looked interesting and is amplified this way. I will put it to you that in practice most p-hacking is not necessarily about hacking p-s but about dredging your data until your results fit a particular pattern. P-hacking refers to when you massage your data and analysis methods until your result reaches a statistically significant p-value. P-hacking and data dredging 12 Nov, 2017 at 14:31 | Posted in Statistics & Econometrics | Comments Off on P-hacking and data dredging ![]()
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