【Translation】What can be done?

What can be done?

我们能做什么?

I’ve discussed many statistical problems throughout this guide. They appear in many fields of science: medicine, physics, climate science, biology, chemistry, neuroscience, and many others. Any researcher using statistical methods to analyze data is likely to make a mistake, and as we’ve seen, most of them do. What can we do about it?

本书中我们已经讨论了很多统计问题。这些问题充斥在各个领域内:医疗、物理、气候科学、生物、化学、神经科学以及其他很多领域。任何一位使用统计方法分析数据的研究人员都有可能犯错,我们也发现大多数人都在犯错。那么我们能为此做些什么呢?

Statistical education

学习统计

Most American science students have a minimal statistical education – perhaps one or two required courses, or even none at all for many students. And even when students have taken statistical courses, professors report that they can’t apply statistical concepts to scientific questions, having never fully understood – or simply forgotten – the appropriate techniques. This needs to change. Almost every scientific discipline depends on statistical analysis of experimental data, and statistical errors waste grant funding and researcher time.

大多数美国理科学生至少要学习一些统计——也许就一两门课,或者大多数人完全不学。即使学生们有统计学科的课程,教授们反馈是他们因为不完全理解相关技术,所以并不会将统计概念应用到科学问题上面去——或者仅仅是因为忘了而已。这一切都需要改变。几乎所有科学研究都倚靠试验数据的统计分析,统计谬误会浪费研究人员大量的人力物力。

Some universities have experimented with statistics courses integrated with science classes, with students immediately applying their statistical knowledge to problems in their field. Preliminary results suggests these methods work: students learn and retain more statistics, and they spend less time whining about being forced to take a statistics course.41 More universities should adopt these techniques, using conceptual tests to see what methods work best.

有些大学尝试将统计课程整合在科学课程里,直接让学生将学习到的统计知识应用在专业领域。前期结果表明这样做是有效的:学生学习和记忆更多统计学知识,就能在进行必修的统计课上少花时间。跟多学校应该采纳这种办法,进行概念性改革看看究竟哪种方法管用。

We also need more freely available educational material. I was introduced to statistics when I needed to analyze data in a laboratory and didn’t know how; until strong statistics education is more widespread, many students will find themselves in the same position, and they need resources. Projects like OpenIntro Stats are promising, and I hope to see more in the near future.

我们同事需要更多免费的教学资料。我最初在实验室进行数据分析的时候,也是一头雾水;直到深入统计学习变得越来越广泛,很多学生都有同样的问题,我们都需要教材来学习。像OpenIntro Stats这样的课程是很棒的,我希望将来看到更多这样的项目。

Scientific publishing

科学出版

Scientific journals are slowly making progress towards solving many of the problems I have discussed. Reporting guidelines, such as CONSORT for randomized trials, make it clear what information is required for a published paper to be reproducible; unfortunately, as we’ve seen, these guidelines are infrequently enforced. We must continue to pressure journals to hold authors to more rigorous standards.

科学期刊在改正这些问题时的脚步就有些缓慢了。报告的指导手册,例如随机试验的CONSORT,能够给可出版文章清晰的要求,确定其出试验结果是可重复的;不幸的是,我们看到,落实这些指导手册的情况太少。我们必须让期刊作者们遵循更严格的标准。

Premier journals need to lead the charge. Nature has begun to do so, announcing a new checklist which authors are required to complete before articles may be published. The checklist requires reporting of sample sizes, statistical power calculations, clinical trial registration numbers, a completed CONSORT checklist, adjustment for multiple comparisons, and sharing of data and source code. The guidelines cover most issues covered in Statistics Done Wrong, except for stopping rules and discussion of any reasons for departing from the trial’s registered protocolNature will also make statisticians available to consult for papers as needed.

核心期刊应该起到带头作用。《自然》已经开始这么做了,他们公布了一项新的检查清单,论文只有在满足清单要求的情况下才有可能被发表。清单中要求报告里包含样本大小、统计功效计算、临床试验注册号、完整的CONSORT检查列表、多重比较的矫正以及数据和源代码的分享。该指导涵盖了大多数本书涵盖的内容,除了停止规则以及关于放弃已注册试验方案的原因。《自然》同时聘请统计学家对需要的论文进行评审。

If these guidelines are enforced, the result will be much more reliable and reproducible scientific research. More journals should do the same.

如果我们强制这些指导手册,科研试验的结果会变得更加可靠和可重复。更多期刊应该跟上这个脚步。

Your job

你可以做的

Your task can be expressed in four simple steps:

你的工作可由以下四个简单的步骤组成:

  1. Read a statistics textbook or take a good statistics course. Practice.

读统计教材或者上统计课程,大量练习。

  1. Plan your data analyses carefully and deliberately, avoiding the misconceptions and errors you have learned.

仔细谨慎地计划数据分析,避免你学到的那些误解与错误。

  1. When you find common errors in the scientific literature – such as a simple misinterpretation of pvalues – hit the perpetrator over the head with your statistics textbook. It’s therapeutic.

当你发下科研文献中的常见错误——例如对p值的误读——拿你的统计教材反击作者,这对他有治愈功效。

  1. Press for change in scientific education and publishing. It’s our research. Let’s not screw it up.

发文章呼吁科学教育和出版的改革。这是我们的研究,我们不能坐视不管。

 

Source:https://www.statisticsdonewrong.com/what-next.html