Article,
Common misconceptions about data analysis and statistics.
The Journal of pharmacology and experimental therapeutics, 351 (1): 200-5 (October 2014)Errors; Anàlisi de dades; Significació estadística<br/><br/>Magnífiques figures per explicar errors freqüents!.
DOI: 10.1124/jpet.114.219170
Abstract
Ideally, any experienced investigator with the right tools should be able to reproduce a finding published in a peer-reviewed biomedical science journal. In fact, however, the reproducibility of a large percentage of published findings has been questioned. Undoubtedly, there are many reasons for this, but one reason may be that investigators fool themselves due to a poor understanding of statistical concepts. In particular, investigators often make these mistakes: 1) P-hacking, which is when you reanalyze a data set in many different ways, or perhaps reanalyze with additional replicates, until you get the result you want; 2) overemphasis on P values rather than on the actual size of the observed effect; 3) overuse of statistical hypothesis testing, and being seduced by the word "significant"; and 4) over-reliance on standard errors, which are often misunderstood.
