title: Common misconceptions about data analysis and statistics.
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.
authors.affiliation: GraphPad Software Inc., La Jolla, California hmotulsky@graphpad.com.
authors.forename: Harvey J
authors.initials: HJ
authors.lastname: Motulsky
mesh_terms: D056808:Biostatistics / Q000379:methods*; D003627:Data Interpretation, Statistical; D015203:Reproducibility of Results