Common misconceptions about data analysis and statistics

doi:10.1007/s00210-014-1037-6

Common misconceptions about data analysis and statistics

2014-09-12

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Harvey Motulsky *

DOI：10.1007/s00210-014-1037-6

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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.

Common misconceptions about data analysis and statistics

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