STATISTICS NOTES Uncertainty and sampling error
STATISTICS NOTES Uncertainty and sampling error
复制标题
DOI：
10.1136/bmj.g7064
复制
发表时间：
2014-11-25
影响因子：
105.7
通讯作者：
Bland, J. Martin
中科院分区：
文献类型：
--
作者：
Altman, Douglas G.;Bland, J. Martin
进行医学研究是为了帮助减少不确定性。例如，随机对照试验旨在回答与特定患者群体的治疗选择有关的问题。然而，一项研究很少能消除不确定性。这有两个原因：抽样误差和其他（非抽样）不确定性来源。“错误”这个词来源于一个拉丁语词根，意思是“徘徊”，我们在统计意义上使用它，意思是平均值的变化，而不是“错误”。抽样误差的产生是因为任何样本的行为可能与抽取它的更大的总体不完全相同。非抽样误差产生于研究可能偏离研究者想要回答的问题的许多方式。抽样误差是统计学家非常关心的问题，他们认为研究中的人群只是感兴趣的总体中许多可能样本中的一个。尽管它受到广泛谴责，1总结研究证据的主要方法是通过P值。很明显，来自研究的证据不能被合理地概括为一个数字，但P值的使用仍然是不可动摇的。此外，将P值标记为显著或不显著的做法不仅会导致二分决策，而且经常会导致相信研究问题已经得到回答。
Medical research is conducted to help to reduce uncertainty. For example, randomised controlled trials aim to answer questions relating to treatment choices for a particular group of patients. Rarely, however, does a single study remove uncertainty. There are two reasons for this: sampling error and other (non-sampling) sources of uncertainty. The word “error” comes from a Latin root meaning “to wander,” and we use it in its statistical sense of meaning variation from the average, not “mistake.” Sampling error arises because any sample may not behave quite the same as the larger population from which it was drawn. Non-sampling error arises from the many ways a research study may deviate from addressing the question that the researcher wants to answer.Sampling error is very much the concern of the statistician, who imagines that the group of people in the study is just one of the many possible samples from the population of interest. Despite it being widely condemned, 1 the dominant way of summarising the evidence from a research study is by the P value. It should be obvious that the evidence from a research study cannot reasonably be summarised as just a single number, but the use of P values remains unshakeable. Further, the practice of labelling P values as significant or not significant leads not only to dichotomous decisions but often also to the belief that the research question has been answered.