芝加哥小熊队夺得棒球世界大赛冠军。
Donald Trump wins the White House.
唐纳德•特朗普入主白宫。
What do those two *epochal events have in common? Both were considered highly unlikely. And both happened.
这两个划时代的事件有什么共同之处呢？它们都被认为是非常不可能实现的，但却都发生了。
Many Americans therefore judge predictions, with more *skepticism. They’ve learned an
因此，很多美国人以更加怀疑的态度来看待预测。在民意测验和其他测量方法的局限性方面，他们已经学到了一个非常重要，甚至是令人欣慰的教训：大数据决定不了命运。
*Algorithms are formulas written by humans to take the guesswork out of what other human beings will do under certain circumstances.
算法是由人类编写的公式，用来猜测其他人类在一定条件下会采取何种行动。
Survey responses to pollsters, consumer buying habits, internet site visits, etc., can be plugged into computer models to suggest people’s future behavior. The understandable hope is always that if you start with knowable measurements and *crunch them through well-constructed formulas, you’ll produce a reliable preview of what will happen.
大选的民意、消费者的购买习惯、互联网网站的访问量等等，针对这些问题的调查结果都会被放到计算机模型中运算，预测出人们未来的行为。人们总是希望从可知的测量方法开始，将测量结果塞到设计完善的公式中，然后就能对将要发生的事做出可靠的预期。心存这种希望是可以理解的。
Not necessarily.
然而事实未必如此。
Computers don’t read minds. Nor do pollsters. People don’t always say what they think. Or they change their minds. People can be convinced and unconvinced. Some people say one thing but do another. You will never write a program to take into account all those *nuances and many others.
电脑无法读懂人们的心思，民意测验专家也无法读懂。人们并不总是心口如一，或者说他们时常改变主意。人们可能会被说服，也可能不会被说服。一些人说一套做一套。你永远无法写出一组程序，将所有这些以及更多的细枝末节考虑进去。
Big Data can lead to Big Mistakes. Google Flu Trends, for instance, sought to use data from internet searches to estimate when *influenza season would peak and at what level. But it drastically overestimated peak flu levels in the 2012-13 season. That failure “doesn’t erase the value of big data,” wrote David Lazer of Northeastern University and Ryan Kennedy of the University of Houston in Wired magazine. “What it does do is highlight a number of problematic practices in its use–what we like to call ‘big data *hubris.’”
大数据可能导致大错误。比如，谷歌流感趋势（Google Flu Trends）希望通过互联网搜索所获取的数据，来预测流感季达到峰值的时间及其强度。但它严重高估了2012至2013流感季病毒的强度。东北大学的大卫•雷泽和休斯敦大学的瑞恩•肯尼迪在《连线》杂志中写道，这次失败“不会抹灭大数据的价值”。“但它确实凸显了大数据在使用中存在的一些问题——我们通常称之为‘大数据浮夸’”。
Should we toss out data and rely only on experience, or on anecdotes, or on what we hear (true or false) from people with whom we agree?
那么我们是否应该丢掉数据，只依赖经验、趣闻轶事，或者从我们认同的人那里听到的信息（可能真实，也可能虚假）？
That would be a dangerous overreaction to the election *flub. If people believe the data cannot be trusted, they may turn instead to “trusting *anecdotes from friends, family and tribe,” political blogger Erick Erickson writes in The New York Times. “Policies will be based on what people think are good ideas, not what data show. This will potentially … further segment an already divided nation,” he warns, aptly.
如果那样做，就是对这次大选的大数据偏差所做出的危险的过度反应。 政治问题博主埃里克•埃里克森在《纽约时报》中这样写道， 如果人们认为数据不可信，那么他们就会转向“信任那些从朋友、家人和社区获得的轶事”“政策的制定将会基于人们认为的不错的主意，而不是数据显示出来的东西。这可能会……进一步使原本就分化的国家更加分裂。”他适时地发出了这样的警告。
Humans embrace Big Data because we live in an unpredictable universe that is often *capricious. People feel comforted when they think they know what is going to happen. They see patterns in random chance. They purge from their thoughts the reality that a 74 percent chance of victory is a 26 percent chance of defeat. *Superstition endures.
我们生活在这样一个变幻莫测、难以预料的世界中，因此，人们欣然拥抱大数据。当人们自以为知道将要发生什么时，他们感到一丝宽慰。他们在随机选择中寻找模式，并且从头脑中抹去了这样一个事实：74的胜算和26的败率是同时并存的。于是对大数据的迷信一直在持续。
Reality is elastic. Every moment brings new possibilities. That’s what makes life intriguing.
事实是有弹性的，每个瞬间都会带来新的可能性。这才让生活充满趣味。
