If your P value looks too good to be true, it probably is: Communicating reproducibility and variability in cell biology
If your P value looks too good to be true, it probably is: Communicating reproducibility and variability in cell biology
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如果您的 P 值看起来好得令人难以置信，那么它可能是：传达细胞生物学的再现性和变异性
DOI：
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发表时间：
2019
期刊：
影响因子：
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通讯作者：
L. Fritz
中科院分区：
文献类型：
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作者：
Samuel J Lord;Katrina B. Velle;R. Mullins;L. Fritz
细胞生物学文献中充斥着错误的微小P值，通常是将单个细胞作为独立样本进行评估的结果。由于读者使用P值和误差条来推断如果重复实验是否可能再次出现报告的差异，因此用于统计检验的样本量N实际上应该是进行实验的次数，而不是所有实验中分析的细胞（或亚细胞结构）的数量。使用细胞数计算的P值不能反映结果的重现性，因此具有高度误导性。为了帮助作者避免这种错误，我们提供了一些示例和实用教程，用于创建传达细胞水平变异性和实验重现性的图形。
The cell biology literature is littered with erroneously tiny P values, often the result of evaluating individual cells as independent samples. Because readers use P values and error bars to infer whether a reported difference would likely recur if the experiment were repeated, the sample size N used for statistical tests should actually be the number of times an experiment is performed, not the number of cells (or subcellular structures) analyzed across all experiments. P values calculated using the number of cells do not reflect the reproducibility of the result and are thus highly misleading. To help authors avoid this mistake, we provide examples and practical tutorials for creating figures that communicate both the cell-level variability and the experimental reproducibility.