%0 Journal Article
%A Motulsky, Harvey J.
%T Common Misconceptions about Data Analysis and Statistics
%D 2014
%R 10.1124/jpet.114.219170
%J Journal of Pharmacology and Experimental Therapeutics
%P 200-205
%V 351
%N 1
%X 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.
%U http://jpet.aspetjournals.org/content/jpet/351/1/200.full.pdf