Merck Research Laboratories, Merck & Co., Inc., 770 Sumneytown Pike, West Point, PA 19486, United States.
Biochem Pharmacol. 2014 Jan 1;87(1):78-92. doi: 10.1016/j.bcp.2013.05.017. Epub 2013 Jun 4.
Descriptive, exploratory, and inferential statistics are necessary components of hypothesis-driven biomedical research. Despite the ubiquitous need for these tools, the emphasis on statistical methods in pharmacology has become dominated by inferential methods often chosen more by the availability of user-friendly software than by any understanding of the data set or the critical assumptions of the statistical tests. Such frank misuse of statistical methodology and the quest to reach the mystical α<0.05 criteria has hampered research via the publication of incorrect analysis driven by rudimentary statistical training. Perhaps more critically, a poor understanding of statistical tools limits the conclusions that may be drawn from a study by divorcing the investigator from their own data. The net result is a decrease in quality and confidence in research findings, fueling recent controversies over the reproducibility of high profile findings and effects that appear to diminish over time. The recent development of "omics" approaches leading to the production of massive higher dimensional data sets has amplified these issues making it clear that new approaches are needed to appropriately and effectively mine this type of data. Unfortunately, statistical education in the field has not kept pace. This commentary provides a foundation for an intuitive understanding of statistics that fosters an exploratory approach and an appreciation for the assumptions of various statistical tests that hopefully will increase the correct use of statistics, the application of exploratory data analysis, and the use of statistical study design, with the goal of increasing reproducibility and confidence in the literature.
描述性、探索性和推断性统计是假设驱动的生物医学研究的必要组成部分。尽管这些工具普遍需要，但药理学中对统计方法的强调已经变得主要由推断性方法主导，而这些方法的选择更多地取决于用户友好型软件的可用性，而不是对数据集或统计测试的关键假设的理解。这种坦率地滥用统计方法学以及追求达到神秘的α<0.05 标准的做法，通过由基本统计培训驱动的不正确分析的发表，阻碍了研究的进展。也许更关键的是，对统计工具的理解不足，使研究人员与自己的数据分离，从而限制了从研究中得出的结论。其结果是研究结果的质量和可信度下降，这引发了最近对高知名度研究结果的可重复性以及随着时间的推移效果似乎减弱的争议。最近“组学”方法的发展导致产生了大量高维数据集，这些问题更加突出，因此需要新的方法来适当地、有效地挖掘这种类型的数据。不幸的是，该领域的统计教育并没有跟上步伐。本评论为直观理解统计学提供了基础，培养了探索性方法，并提高了对各种统计测试假设的认识，希望这将增加统计数据的正确使用、探索性数据分析的应用以及统计研究设计的使用，以提高文献的可重复性和可信度。
