????商業建議類書籍的書評人總有汗牛充棟的資料供其研究。每每一本書遞送至家門口時，它看上去總像是最后一本。所有的戰略處方皆有全面的研究支持，每位作者似乎都具備令人敬畏的資質。出于兩個原因，我們很難確定誰正在為相關討論增添價值。其一，沒有人有那么多時間讀完所有這些著作；其二，作者往往有很強的動機從令人生厭的數據中歸納出確鑿的結論。

????“為了支持一個假設，作者總是自覺或下意識地給數據強加一個其實并不存在的理論模型，”邁克爾?雷諾說。“畢竟，如果你盯著一塊磅餅足夠長時間，貓王的輪廓就一定會出現（流行音樂巨星貓王以喜歡磅餅著稱——譯注）。”雷諾曾與蒙塔?艾哈邁德合作撰寫了《三個規則：卓越的公司如何思考》(The Three Rules: How Exceptional Companies Think)一書。

????《三個規則》同樣遵循了這類書籍的常規范式：援引令人印象深刻的研究數字（2.5萬家公司45年的發展歷程），然后使用幾頁的篇幅闡述其研究方法。于是，我詢問了雷諾一個問題：他自己是如何閱讀商業書籍的？有沒有一種方式讓我們謙卑且迅速地評價研究結論，但同時又不放棄質疑精神呢？

????雷諾的第一個處方是：務必記住，有說服力的故事需要講故事的人忽略雜音。對于公司傳記類書籍來說，這一點尤為中肯，因為這種體裁需要講述者省略在事后看來無關宏旨的人物和事件。

????他的第二個告誡與如何評價作者的因果關系論斷有關。大多數聰明人都知道，不要把相互關系錯誤地理解為因果關系，但我們一直都在犯這個錯誤。或者，我們常常以其他人沒有證明因果關系為由，不予理會他們的論斷（僅僅因為某一個事件發生在另一個事件之后并不意味著前者導致了后者的發生）。的確如此，雷諾說，但“沒有人能夠拿出因果關系的證據。”因果關系確實存在——要是不存在的話，人們恐怕就沒有那么大的激勵一大早離開家去工作了——但在一個復雜的系統中，我們很難證明這一點。需要說明的是，任何有人類存在的系統皆是復雜的系統。

????此外，雷諾還建議我們小心提防菲爾?羅森茨維格所稱的“暈輪效應”(the halo effect)。換句話說，一定不要讓一家公司在某個領域的標志性成就所反射的榮耀影響你評價它在其他領域的表現。

????接下來要注意數據和你自身的局限性。為什么要充分考慮自身的局限性呢？看到具有統計意義的數據時，我們的直覺或許是非常可怕的。當我們捧起一本闡述某些公司的書籍時，我們傾向于假定這些正在被作者詳細分析的公司其實已經取得了值得關注的成就。

????但“這是一個值得質疑的假設，”雷諾說。“如果兩家公司的盈利能力差異微乎其微，比如說，某個五年期間內的資產收益率相差0.1%，那么你是否會悉心研究這兩家公司，以理解導致業績差異的行為差異呢？當然不會。因為這個時間段太短，而這個差異又幾乎可以忽略不計。”

????所以，我們一定要留意樣本的選擇，以及分析的時間框架。“在一個很短的時期內，運氣成分很可能大于技能因素。”

????雷諾的最后一個建議涉及商業成功案例研究頻頻遭到指摘的一面。一家受到某本流行商業書籍稱贊的公司——比如吉姆?柯林斯在其2001年的著作《從優秀到卓越》(Good to Great)一書中表揚過的電器城公司( Circuit City)——最終令人大失所望。批評家們隨后一擁而上，紛紛指責作者搞砸了研究（“嘿嘿，等一下，你不是說這家公司非常了不起嗎，怎么才過了三年，它就破產了呢？你其實并不知道你自己在說什么。”）這種評價不僅有失公允，而且相當短視。雷諾說：“在他們看來，首先必須好好研究一家永遠都表現優異的公司，然后才可以從中提煉出某種結論。這種觀點完全是無稽之談。”???

????People in the business of reviewing business advice get it by the truckload. After a while, every book delivered to your door looks just like the last one. All strategy prescriptions are backed by comprehensive research and every author is impressively credentialed. It is hard to determine who is adding value to the conversation for two reasons: One, no one has time to read all these books; two, there's tremendous incentive for an author to spin hard conclusions out of mucky data.

????"It's very tempting, consciously or subconsciously, to impose a pattern on data that isn't really there in order to support a hypothesis," says Michael Raynor, co-author with Mumtaz Ahmed of The Three Rules: How Exceptional Companies Think. "After all, if you stare at the poundcake long enough, Elvis's profile will surely appear."

????The Three Rules conforms to type by citing impressive study numbers -- 25,000 companies over 45 years -- then allocates several pages to unpacking their study methodology. So I asked Raynor how he reads business books. Is there a way to assess research claims quickly, respectfully, but skeptically?

????Raynor's first prescription is to remember that persuasive storytelling requires that the storyteller leave out the weeds. This is especially relevant to corporate biographies, since the form requires the narrator to omit people and events that turn out to be irrelevant only in hindsight.

????His second note of precaution is about what to do when presented with causal claims. Most smart people know not to mistake correlation for causality, but we do it all the time. Or we dismiss someone else's claims by saying that they haven't proved causality (just because one event happened after another doesn't mean the first happening caused the second). True enough, says Raynor, but "nobody has evidence of causality." Causation exists -- there would be less incentive to leave the house in the morning if it didn't -- but it's difficult to prove in complex systems (and any system that includes humans is a complex system).

????Raynor also advises watching out for what Phil Rosenzweig dubbed "the halo effect." In other words, make sure you aren't letting the reflected glory of a company's signature achievement in one arena color your view of their performance in other areas.

????Next, be aware of the data's limitations and your own. Why dwell on your own limitations? Our intuition as to what's statistically significant can be terrible. When we pick up a book that profiles certain companies, we tend to assume that the companies being profiled have, in fact, delivered noteworthy performance.

????But "that's an assumption that's worth questioning," says Raynor. "If two companies differ in profitability by 0.1% in return on assets over a five-year period, would you study those two companies to understand behavioral differences that drive performance differences? Of course not. Because it's too small a difference over too short a period of time."

????So watch for sample selection and time frame. "In a short season, luck can overcome skill."

????Raynor's last note concerns an all too common criticism of business success studies. Say a company praised in a popular business book -- for example, Circuit City in Jim Collins's 2001Good to Great -- ultimately disappoints. Critics then pile on to say that the author botched the analysis. ("Hey wait a minute, you said that company was great and then three years later they're in bankruptcy. You don't know what you're talking about.") That's unfair – and shortsighted. "This whole notion that you have to study a company that is perpetually excellent before you can learn something [from them] is nonsense," Raynor says.