![]() ![]() ‘false positives’) and is thus unreliable. Such an analysis can often generate statistically significant results in absence of a true effect (i.e. While many different choices might be defensible, a canonical case of p-hacking would involve trying out multiple different options and reporting the result which yields the lowest p-value (particularly when alternative choices generate values that do not yield a significant result). how to handle outliers, whether to combine groups, including/excluding covariates) which will produce a statistically significant p-value. ![]() In contrast, p-hacking occurs when an initial analysis produces results which are close to being statistically significant, then, in absence of a study protocol, researchers can make analytic choices (e.g. Ideally, these choices are guided by the principles of best practice and prespecified in a publicly available protocol. For example, in nearly any analysis of data there are several “researcher degrees of freedom”- i.e., choices that must be made in the process of analysis. fishing, p-hacking), but each essentially involves probing the data in unplanned ways, finding and reporting an “attractive” result, without accurately conveying the course of analysis. He is 玉山学者 (Yushan Scholar), web page hu.Data-dredging bias is a general category which includes a number of misuses of statistical inference (e.g. He has created the a financial risk meter, FRM hu.berlin/frm, a cryptocurrency index, CRIX organises regularly . He has professional experience in financial engineering, SMART (Specific, Measurable, Achievable, Relevant, Timely) data analytics, machine learning and cryptocurrency markets. ![]() ![]() He is highly ranked and cited on Google Scholar, REPEC and SSRN. He has published over 30 books and more than 300 papers in top statistical, econometrics and finance journals. His research focuses on data sciences, dimension reduction and quantitative finance. He is guest professor at WISE, Xiamen U, SMU, Singapore, NYCU, Hsinchu TW, Charles U, Prague CZ. He also serves as head of the joint BRC (with U Zürich). He is Ladislaus von Bortkiewicz Professor of Statistics at Humboldt-Universität zu Berlin and the director of the Sino German Graduate School (洪堡大学 + 厦门大学) IRTG1792 on “High dimensional non stationary time series analysis”. in Mathematics at Universität Heidelberg in 1982 and in 1988 his habilitation at Universität Bonn. ![]()
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