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民调平均值赢了。然后呢?The polling average won. Now what?

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2026年10月27日星期二,在佛罗里达州多拉尔,人们走出选举监督官办公室,当时正值2026年中期选举提前投票期间。(美联社照片/琳恩·斯拉德基)People walk from the Office of the Supervisor of Elections during early voting in the 2026 midterm elections, Tuesday, Oct. 27, 2026, in Doral, Fla. (AP Photo/Lynne Sladky)

民调平均值胜出了。二十年前,我在《华盛顿邮报》上担忧,对民调平均的新热潮将每项调查都视为同等成分,我写道:“我们需要的是标准,而非平均值。”如今,平均值每小时更新,预测市场赔率持续变动,参议院席位优势午餐前就能易手。我们比以往任何时候都更清楚谁领先、谁落后,但对“为什么”领先的理解却减弱了。

The polling average won. Twenty years ago, in The Washington Post, I worried that the new fashion for averaging treated every survey as an equal ingredient, and I wrote, "We need standards, not averages." Today the averages update by the hour, prediction-market odds move continuously, and a Senate margin can change before lunch. We have a better sense than ever of who is up and who is down, but a diminished sense of why.

我们并未因竞选活动而更知情。17% 不认同特朗普总统的缅因州选民计划投票给共和党现任参议员苏珊·柯林斯。在如此胶着的竞选中,他们可能决定谁控制参议院。2020 年,拜登在缅因州以 9 个百分点获胜,柯林斯仍以接近的优势连任。民调告诉我们这些选民的存在。平均值和市场却将他们的理由排除在外。这本应是探究的起点。

We are not better informed about the campaigns. Seventeen percent of Maine voters who disapprove of President Trump plan to vote for Sen. Susan Collins, the Republican incumbent. In a race this close, they could decide who controls the Senate. In 2020, Joe Biden carried Maine by 9 points, and Collins still won by nearly as much. The polls tell us these voters are there. The averages and markets leave their reasons out of the picture. That should be the beginning of an inquiry.

9 月《纽约时报》/《波特兰先驱报》/锡耶纳学院的民调显示柯林斯领先 3 个百分点,处于误差范围内。此后大多数民调显示两人打平,或民主党人特洛伊·杰克逊占优。她自己选民中的分裂保持不变:约三分之一的柯林斯支持者不认同特朗普,只有半数表示更希望参议员支持他。交叉表提供了线索:选民视她为更有效的参议员,近三分之二的柯林斯选民称杰克逊过于偏左。

A September New York Times/Portland Press Herald/Siena poll had Collins up 3 points, inside the margin of error. Most polls since show the race tied or give Democrat Troy Jackson an edge. The split among her own voters has held: About a third of Collins supporters disapproved of Trump, and only half said they'd rather have a senator who supports him. The tables offer clues: Voters saw her as the more effective senator, and nearly two-thirds of her voters called Jackson too far to the left.

在 9 月初 CNN 的一项民调中,多数柯林斯选民表示他们的投票根本不是关于特朗普的信号。但没有任何公开的交叉表显示,不认同特朗普的柯林斯选民是如何回答这些问题的。报道止步于领先幅度。11 月 3 日,部分预估会准确,部分会出错;我们将花费数周争论哪个对哪个错。这是错误的争论。真正的较量在于结果意味着什么。

In an early-September CNN poll, a majority of Collins voters said their vote wasn't a message about Trump at all. But no published table shows how Collins voters who disapprove of Trump answered any of these questions. The coverage stops at the margin. On Nov. 3, some of the estimates will be right, and some will be wrong; we will spend weeks arguing about which. That’s the wrong fight. The real battle is over what the results mean.

谁获得授权,授权做什么?选民想要什么样的候选人,什么样的立场?这场辩论通常靠轶事和说辞驱动。

Who has a mandate, and for what? What kinds of candidates do voters want, and which positions? That debate usually runs on anecdote and spin.

新闻报道应当基于数据,而数据是存在的。以数据中心为例。没有单一民调能讲清这个故事,但十几个民调可以。盖洛普、福克斯新闻、埃默森以及《经济学人》/优戈夫今年都发现,全国范围内多数民众反对在家附近建设数据中心,比例在 63% 到 71% 之间;奎尼皮亚克大学民调显示,宾夕法尼亚州有 74% 的人持反对态度。

It should run on data, and the data exist. Take data centers. No single poll tells that story. A dozen do. Gallup, Fox News, Emerson, and The Economist/YouGov all found national majorities this year against a data center near home, from 63 to 71 percent; Quinnipiac found 74 percent in Pennsylvania.

Heatmap 和 Embold Research 自 2025 年 8 月以来四次提出同一个问题,反对比例从 42% 上升到 75%。一名在这一议题上获胜的候选人,可以宣称拥有授权,阻止数据中心在选民自家后院建设。但继续看其余数据表,授权的含义就不一样了。共同的主线是电费。皮尤研究中心发现,听说过数据中心的美国人认为其对家庭能源成本“不利”的可能性,是认为“有利”的六倍。

Heatmap and Embold Research have asked the same question four times since August 2025, and opposition rose from 42 percent to 75 percent. A candidate who wins on that could claim a mandate to stop data centers from being built in their constituents' backyards. Read the rest of the tables, and the mandate looks different. The common thread is the electric bill. Pew found Americans who had heard of data centers six times as likely to call them bad for home energy costs as good.

优戈夫民调显示,六成受访者认为附近建数据中心会推高他们支付的费用。而当 Echelon Insights 让选民假设数据中心自行承担电网成本、电费不上涨时,54% 认为地方官员应予许可,34% 主张禁止。反对声浪是头条,但这些民调综合起来,给获胜者提出了另一个问题:如果开发商承担成本,选民会接受数据中心吗?

Six in 10 told YouGov a nearby one would raise what they pay. And when Echelon Insights asked voters to assume data centers would cover their own grid costs so bills did not rise, 54 percent said local officials should allow them; 34 percent said ban them. Opposition is a headline. Together, these polls suggest a different question for the winners: Would voters accept data centers if developers paid the costs?

这些数据表大多公开。AI 能在几秒钟内搜索它们,把十几个民调的发现汇总,帮我们看清答案在哪里趋同、在哪里分歧、还有什么没人问过。AI 也可能看错一栏数据,或把两个群体搞混。那份 CNN 民调发现,30% 的柯林斯选民不赞同特朗普。换个问法,20% 的特朗普不赞同者支持柯林斯。两者都是真的。搞混分母,讲出的就是错故事。

Most of those tables are public. AI can search them in seconds. It can bring findings from a dozen polls together and help us see where the answers converge, where they differ, and what no one has asked. AI can also misread a column or mix up two groups. That CNN poll found that 30 percent of Collins voters disapproved of Trump. Turn the question around, and 20 percent of Trump disapprovers supported Collins. Both are true. Confuse the denominators, and you will tell the wrong story.

所以我 2006 年提出的规则依然适用:民调机构要披露,新闻编辑部要把关。当 AI 找出一个有趣的数字,记者应当能看到确切的提问措辞、谁作了答、以及它在数据表的什么位置。如果缺了其中任何一样,这个数字就不具备刊发条件。

So the rules I asked for in 2006 still apply: disclosure from pollsters, judgment from newsrooms. When AI turns up an interesting number, a reporter should be able to see the exact question, who answered it, and where it appears in the tables. If any of that is missing, the number isn't ready for print.

如果验证无误,就用它来设计下一个问题。重新联系愿意配合的受访者,或者抽取一个新的样本,其中包含足够多的这类交叉压力选民,听听他们的想法。让他们用自己的语言解释,为什么这样投票,以及什么能改变他们的主意。竞选过程能教会我们东西。我们的问卷也应该学习。2006年我太追求整齐划一了。好的平均值自有其地位。

If it checks out, use it to write the next question. Recontact willing respondents, or draw a fresh sample with enough of these cross-pressured voters to hear them out. Let them explain, in their own words, why they vote the way they do and what could change their minds. A campaign teaches us things. Our questionnaires should learn, too. I was too neat in 2006. Good averages have earned their place.

但它们从来不是为了告诉我们选民想要什么而设计的,市场给结果定价,而非给理由定价。两者都赢了。下一步是深入表面之下。当数据表公开、工具如此好用时,止步于边际是一种懒惰的选择。乔恩·科恩创立了TrueDot,一家AI加速研究公司。他曾于2006年至2013年担任《华盛顿邮报》民调总监,后来担任皮尤研究中心研究副总裁和SurveyMonkey首席研究官。

But they were never built to tell us what voters want, and the markets price outcomes, not reasons. Both have won. The next step is to dig beneath the surface. When the tables are public and the tools are this good, stopping at the margin is a lazy choice. Jon Cohen founded TrueDot, an AI-accelerated research company. He was The Washington Post's polling director from 2006 to 2013 and later was vice president of research at the Pew Research Center and chief research officer at SurveyMonkey.

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