摘要翻译：
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。因为读者使用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，所以用于统计测试的样本量N实际上应该是进行实验的次数，而不是在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复性，因此具有很强的误导性。为了帮助作者避免这个错误，我们提供了例子和实用的教程来创建传达细胞水平可变性和实验重复性的图形。
---
英文标题：
《If your P value looks too good to be true, it probably is: Communicating
  reproducibility and variability in cell biology》
---
作者：
Samuel J. Lord, Katrina B. Velle, R. Dyche Mullins, Lillian K.
  Fritz-Laylin
---
最新提交年份：
2019
---
分类信息：

一级分类：Quantitative Biology        数量生物学
二级分类：Other Quantitative Biology        其他定量生物学
分类描述：Work in quantitative biology that does not fit into the other q-bio classifications
不适合其他q-bio分类的定量生物学工作
--

---
英文摘要：
  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.
---
PDF链接：
https://arxiv.org/pdf/1911.03509