
That bon mot, now widely quoted, originated with W. Edwards Deming, who was arguably America's greatest statistician.
The irony is that Deming was a prophet without honor in his own country. His penchant for precision and prickly insistence on facts made many executives of his day uncomfortable. Most preferred camaraderie and the judgment with which they believed their experience had blessed them. 'You can never get fired for buying IBM,' was another expression of this mindset.
So Deming went where he was wanted - and needed. In the late 50s and early 60s, that was Japan. A decade or so later, that country's manufacturers began their determined and largely successful assault on the global economy.
Since that time data has enjoyed an honored place in the pantheon of business decision making. Today, the notion of Big Data dominates discussion of the future for those managing the tech/business interface. And that is, by and large, a good thing.
There is just one issue; successful application of the wisdom embedded in data requires judgment. So the interpretation of data depends upon careful analysis, a keen eye for anomalies and a skeptical attitude about anything smacking of certainty or the notion that 'forever' is a logical concept.
As development of tech products becomes faster, cheaper and more powerful, this approach puts ever greater pressure on those charged with making larger decisions with greater financial implications in less time. This is the story of one such set decision-making criteria. It's ultimate veracity has yet to be determined, but its application can be evaluated in context now. JL
Brian Christian reports in Wired:
Over the past decade, the power of A/B testing has become an open secret of high-stakes web development. It’s now the standard (but seldom advertised) means through which Silicon Valley improves its online products. Using A/B, new ideas can be essentially focus-group tested in real time: Without being told, a fraction of users are diverted to a slightly different version of a given web page and their behavior compared against the mass of users on the standard site. If the new version proves superior—gaining more clicks, longer visits, more purchases—it will displace the original; if the new version is inferior, it’s quietly phased out without most users ever seeing it.
A/B allows seemingly subjective questions of design—color, layout, image selection, text—to become incontrovertible matters of data-driven social science.


























