Common misconceptions about data analysis and statistics

Naunyn Schmiedebergs Arch Pharmacol. 2014 Nov;387(11):1017-23. doi: 10.1007/s00210-014-1037-6. Epub 2014 Sep 12.


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, the reproducibility of a large percentage of published findings has been questioned. Undoubtedly, there are many reasons for this, but one reason maybe that investigators fool themselves due to a poor understanding of statistical concepts. In particular, investigators often make these mistakes: 1. P-Hacking. This 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". 4. Overreliance on standard errors, which are often misunderstood.

MeSH terms

  • Data Interpretation, Statistical*
  • Humans
  • Peer Review, Research
  • Periodicals as Topic / standards*
  • Periodicals as Topic / statistics & numerical data
  • Reproducibility of Results