The idea arose because of the perplexing (第四题答案为B)behaviour of the women (who assembled relays and wound coils of wire)(题目中此处删除) in the Hawthorne plant. According to accounts (第五题答案为C)of the experiments, their hourly output rose when lighting was increased, but also when it was dimmed. It did not matter (第六题答案为B)what was done; so long as (第七题答案为D)something was changed, productivity rose. An awareness (第八题答案为A)that they were being experimented upon seemed to be enough (第九题答案为C)to alter workers' behaviour by (第十题答案为D)itself。
So, should you hire generalists or specialists? It really does depend—and the largest factor in your decision should be your company’s stage of maturity. But if you're still not sure, then I suggest you favor generalists, especially if your company is still in a stage of rapid growth. Your problems are probably not as specialized as you think, and hiring generalists reduces your risk. Plus, hiring generalists allows you to give them the opportunity to learn specialized skills on the job. Everybody wins.
Another of the original observations was that output fell when the trials ceased, suggesting that the act of experimentation caused increased productivity. But experimentation stopped in the summer, and it turns out from the records of production after the experiments that output tended to fall in the summer anyway. Perhaps workers were just hot。
How do you identify a good generalist? Ideally this is someone who has already worked with data sets that are large enough to have tested his or her skills regarding computation, quality, and heterogeneity. Surely someone with a STEM background, whether through academic or on-the-job training, would be a good candidate. And someone who has demonstrated the ability and willingness to learn how to use tools and apply them appropriately would definitely get my attention. When I evaluate generalists, I ask them to walk me through projects that showcase their breadth.
WHEN America's National Research Council sent two engineers to supervise a series of industrial experiments at a large telephone-parts factory called the Hawthorne Plant near Chicago in 1924, it hoped they would learn how shop-floor lighting affected (第一题答案为A)workers' productivity. Instead, the studies ended up (第二题答案为B)giving their name to the "Hawthorne effect", the extremely influential idea that the very act (第三题答案为C)of being experimented upon changes subjects' behaviour。
Editor's note: This is the second in a three-part series of posts by Daniel Tunkelang dedicated to data science as a profession. In this series, Tunkelang will cover the recruiting, organization, and essential functions of data science teams.
命题专家改写了下面的句子(The data from the illumination experiments had never been rigorously analysed and were believed lost. But Steven Levitt and John List, two economists at the University of Chicago, discovered that the data had survived the decades in two archives in Milwaukee and Boston,) and decided to subject (第十一题答案为C)them to econometric analysis. The Hawthorne experiments had another surprise in store for them. Contrary to (第十二题答案为A)the descriptions in the literature, they found no systematic evidence (第十三题答案为A)that levels of productivity in the factory rose whenever changes in lighting were implemented。
2010年的考研英语完型填空部分，使用了2009年6月6日 Economist 《经济学人》杂志上的一篇文章，文章主要内容，是对社会学上一个经典的理论：霍桑效应的批判和反思。文章难度适中。命题专家在出题的时候也进行了一定程度的改写。下面结合原文，我来公布一下标准答案。
If you are building a team of data scientists, should you hire generalists or specialists? As with most things, it depends. Consider the kinds of problems your company needs to solve, the size of your team, and your access to talent. But, most importantly, consider your company's stage of maturity.
Similarly, having statistical expertise on staff becomes critical when you are running thousands of simultaneous experiments and worrying about interactions, novelty effects, and attribution. These are first-world problems, but they are precisely the kinds of problems that call for senior statisticians.
June 6, 2009
Article image: Chess Knights In Battle. (source: By Ken Teegardin on Flickr).
Hence, the person building the product doesn't need to have a Ph.D. in statistics or 10 years of experience working with machine learning algorithms. What's more useful in the early days is someone who can climb around the stack like a monkey and do whatever needs doing, whether it’s cleaning data or native mobile app development.
It turns out that idiosyncrasies in the way the experiments were conducted may have led to misleading (第十四题答案为D)interpretations of what happened. For example(第十五题答案为B)， lighting was always changed on a Sunday, when the plant was closed. When it reopened on Monday, output duly rose (第十六题答案为A)compared with Saturday, the last working day before the change, and continued (第十七题答案为D)to rise for the next couple of days. But (第十八题答案)a comparison with data for weeks when there was no experimentation showed that output always went up on Mondays. Workers tended to(第十九题答案) beaver away(题目中换成了较简单的be diligent) for the first few days of the working week in any case, before hitting (第二十题答案为D)a plateau and then slackening off。
There is a suggestion in the data that productivity was more responsive to changes in artificial than natural light. This could be interpreted as a subtler version of the Hawthorne effect, if you believe that workers were aware that changes in artificial light were induced by the experimenters, whereas natural light was changing on its own. But even this evidence is weak. For something so influential and intuitively appealing, it turns out that the Hawthorne effect is remarkably hard to pin down。
December 17, 2015
Light work; Questioning the Hawthorne effect
第2篇 of Daniel Tunkelang--Data Scientists: Generalists or specialists?