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外文期刊>Pharmacology Research & Perspectives
>Common misconceptions about data analysis and statistics This commentary evolved from multiple conversations between the author and several editors of pharmacology journals. This article is being simultaneously published in Br J Pharmacol, J Pharmacol Exp Ther, Naunyn‐Schmiedeberg's Arch Pharmacol and Pharmacol Res Perspect in a collaborative effort to help investigators and readers appropriately use and interpret statistical analyses in pharmacological research studies.
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Common misconceptions about data analysis and statistics This commentary evolved from multiple conversations between the author and several editors of pharmacology journals. This article is being simultaneously published in Br J Pharmacol, J Pharmacol Exp Ther, Naunyn‐Schmiedeberg's Arch Pharmacol and Pharmacol Res Perspect in a collaborative effort to help investigators and readers appropriately use and interpret statistical analyses in pharmacological research studies.
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, 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. 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. e00093.
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