Would it be wise to loan for a vocation?

Will you invest in people who loan for vocations? 

For years I’ve been fascinating about this culture conflict in consumption habits: westerners’ borrowing versus Chinese’s saving habits.

China enjoys the highest personal saving rates over the world. Most people put almost 50% of their income into saving accounts. Comparably, the average U.S. personal savings rate has been around 5% for the last few years. Chinese would save every dollar for housing, education and health care, if not the last, travel would never be of the first priority, let alone borrow money for it. However I notice there is a great amount of money borrowed by kiwis for holiday expenses (the third largest proportion among all purposes) in one of the p2p platform in New Zealand. Continue reading Would it be wise to loan for a vocation?




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。 Continue reading 【Translation】Conclusion

【Translation】What can be done?

What can be done?


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? Continue reading 【Translation】What can be done?

【Translation】What have we wrought?

What have we wrought?


I’ve painted a grim picture. But anyone can pick out small details in published studies and produce a tremendous list of errors. Do these problems matter?


Well, yes. I wouldn’t have written this otherwise.

当然。否则我也不会写出这本书。 Continue reading 【Translation】What have we wrought?

【Translation】Hiding the data 

Hiding the data 


“Given enough eyeballs, all bugs are shallow.”

—Eric S. Raymond


——Eric S. Raymond

We’ve talked about the common mistakes made by scientists, and how the best way to spot them is a bit of outside scrutiny. Peer review provides some of this scrutiny, but a peer reviewer doesn’t have the time to extensively re-analyze data and read code for typos – reviewers can only check that the methodology makes good sense. Sometimes they spot obvious errors, but subtle problems are usually missed.52

我们谈论了科学家常犯的错误,以及识别他们最好的办法就是旁观者的审视。同行评审提供部分审查,但是同行评审并没有花时间去广泛地重新分析数据,读代码纠错——评审只是在检查作者使用的方法是否符合逻辑。有时也会发现一些明显的错误,但是一些轻微的问题就被忽略不见了。 Continue reading 【Translation】Hiding the data 

【Translation】Everybody makes mistakes

Everybody makes mistakes


Until now, I have presumed that scientists are capable of making statistical computations with perfect accuracy, and only err in their choice of appropriate numbers to compute. Scientists may misuse the results of statistical tests or fail to make relevant computations, but they can at least calculate a p value, right?


Perhaps not.

也许并不。 Continue reading 【Translation】Everybody makes mistakes

【Translation】Researcher freedom: good vibrations?

Researcher freedom: good vibrations?


There’s a common misconception that statistics is boring and monotonous. Collect lots of data, plug the numbers into Excel or SPSS or R, and beat the software with a stick until it produces some colorful charts and graphs. Done! All the statistician must do is read off the results.

人们总是误以为统计是无聊和单调的。收集大量的数据,然后倒入Excel或者SPSS、R软件里,折磨机器好一阵子直到得出漂亮的图表。完美!所有统计学家必须要做的是有可读性的结论。 Continue reading 【Translation】Researcher freedom: good vibrations?

【Translation】Stopping rules and regression to the mean

Stopping rules and regression to the mean


Medical trials are expensive. Supplying dozens of patients with experimental medications and tracking their symptoms over the course of months takes significant resources, and so many pharmaceutical companies develop “stopping rules,” which allow investigators to end a study early if it’s clear the experimental drug has a substantial effect. For example, if the trial is only half complete but there’s already a statistically significant difference in symptoms with the new medication, the researchers may terminate the study, rather than gathering more data to reinforce the conclusion.


When poorly done, however, this can lead to numerous false positives.

然而,如果做的不合适,这种终止规则会导致大量的假阳性结论。 Continue reading 【Translation】Stopping rules and regression to the mean

【Translation】When differences in significance aren’t significant differences

When differences in significance aren’t significant differences


“We compared treatments A and B with a placebo. Treatment A showed a significant benefit over placebo, while treatment B had no statistically significant benefit. Therefore, treatment A is better than treatment B.”


We hear this all the time. It’s an easy way of comparing medications, surgical interventions, therapies, and experimental results. It’s straightforward. It seems to make sense.


However, a difference in significance does not always make a significant difference.22

然而,那些看上去显著的差异有时候并不算真正意义的显著差异。 Continue reading 【Translation】When differences in significance aren’t significant differences

【Translation】Red herrings in brain imaging

Red herrings in brain imaging


Neuroscientists do massive numbers of comparisons regularly. They often perform fMRI studies, where a three-dimensional image of the brain is taken before and after the subject performs some task. The images show blood flow in the brain, revealing which parts of the brain are most active when a person performs different tasks.

神经学家在研究过程中做了大量的对比。例如fMRI研究中,在测试者实施某项活动前后对其脑部进行三维造影。影像显示了大脑血液的流动,揭示了人类在完成不同任务的过程中大脑哪一部分最活跃。 Continue reading 【Translation】Red herrings in brain imaging