【Translation】Conclusion

Conclusion

总结

Beware false confidence. You may soon develop a smug sense of satisfaction that your work doesn’t screw up like everyone else’s. But I have not given you a thorough introduction to the mathematics of data analysis. There are many ways to foul up statistics beyond these simple conceptual errors.

时刻小心你的过于自信。你可能会因为自己没像其他人一样搞砸而沾沾自喜。但是我们并没有深入数据分析背后的数学问题。有太多可能会导致这些常见的概念错误。

Errors will occur often, because somehow, few undergraduate science degrees or medical schools require courses in statistics and experimental design – and some introductory statistics courses skip over issues of statistical power and multiple inference. This is seen as acceptable despite the paramount role of data and statistical analysis in the pursuit of modern science; we wouldn’t accept doctors who have no experience with prescription medication, so why do we accept scientists with no training in statistics? Scientists need formal statistical training and advice. To quote:

错误之所以常见,是因为很少有理科或者医学院要求本科生学习统计和试验设计——一些入门的统计课程又跳过了统计功效和多重推论的概念。他们取代了数据与统计分析的重要角色,在当代科学研究中被广泛接受;我们不接受一个没有开过处方经验的医生,那么我们为什么会接受没有经过统计训练的科学家呢?科学家需要接受正式的统计训练和教育。有人这样说:

“To consult the statistician after an experiment is finished is often merely to ask him to conduct a post mortem examination. He can perhaps say what the experiment died of.”

—R. A. Fisher, popularizer of the p value

“在试验结束后咨询统计学家意见无异于一场验尸检查。他完全可以告诉你这个试验是怎么被毙掉的。”

—R. A. Fisherp值推广者

Journals may choose to reject research with poor-quality statistical analyses, and new guidelines and protocols may eliminate some problems, but until we have scientists adequately trained in the principles of statistics, experimental design and data analysis will not be improved. The all-consuming quest for statistical significance will only continue.

期刊编辑也许会拒绝那些低质量的统计分析研究,新的指导手册和规范也许会杜绝一些问题,但是只有我们的科学家受到足够的统计、试验设计和数据分析的培训,现实才能真正有所改进。在寻找统计显著性的道路上人们还是会不懈余力的。

Change will not be easy. Rigorous statistical standards don’t come free: if scientists start routinely performing statistical power computations, for example, they’ll soon discover they need vastly larger sample sizes to reach solid conclusions. Clinical trials are not free, and more expensive research means fewer published trials. You might object that scientific progress will be slowed needlessly – but isn’t it worse to build our progress on a foundation of unsound results?

任何改变都不是轻松的。严谨的统计标准不是随便得来的:如果科学家开始将统计功效的计算作为例行公事,例如,他们很快就会发现要得到靠谱的结果,他们需要更大的样本空间。临床试验不是免费的,这意味着研究经费的增长,直接过导致更少的试验会被发表。你也许会觉得科学发展进度会放缓而反对——但是建立在不牢靠的研究成果上,发展再迅速不是更糟糕吗?

To any science students: invest in a statistics course or two while you have the chance. To researchers: invest in training, a good book, and statistical advice. And please, the next time you hear someone say “The result was significant with p<0.05, so there’s only a 1 in 20 chance it’s a fluke!”, please beat them over the head with a statistics textbook for me.

这里告诫所有学习科学的同学们:认真学习一两门统计课程。对所有的研究人员:投入精力进行训练,读一本好的书,听取统计学家的意见。再次恳请,下次当你听到有人说:“因为p<0.05,统计显著性,所以只有二十分之以的概率是因为侥幸!”请替我大胆指出他们的错误(拿起你们的统计课本敲他们的头)。

Disclaimer: The advice in this guide cannot substitute for the advice of a trained statistical professional. If you think you’re suffering from any serious statistical error, please consult a statistician immediately. I shall not have any liability from any injury to your dignity, statistical error or misconception suffered as a result of your use of this website.

声明:本书中的建议并不能替代专业统计学家的建议。如果你觉得你正在遭受某个统计问题的折磨,请立刻咨询统计学家。任何使用此网站带来的精神损失、统计谬误或误解,恕我无法负责。

Use of this guide to justify rejecting the results of a scientific study without reviewing the evidence in any detail whatsoever is grounds for being slapped upside the head with a very large statistics textbook. This guide should help you find statistical errors, not allow you to selectively ignore science you don’t like.

不进行任何细节的评估,仅仅是使用本书去反对一项研究的成果,本身就是用统计教科书给自己打脸的行为。这本书是想帮助大家找统计谬误,而非给你为自己拒绝不爱的课题一个理由。