PT - JOURNAL ARTICLE
AU - Motulsky, Harvey J.
TI - Common Misconceptions about Data Analysis and Statistics
AID - 10.1124/jpet.114.219170
DP - 2014 Oct 01
TA - Journal of Pharmacology and Experimental Therapeutics
PG - 200--205
VI - 351
IP - 1
4099 - http://jpet.aspetjournals.org/content/351/1/200.short
4100 - http://jpet.aspetjournals.org/content/351/1/200.full
SO - J Pharmacol Exp Ther2014 Oct 01; 351
AB - 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.