24 Mar 2014

“Lies, Damned Lies, and Statistics”

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Judith Levy, editor of Ricochet, went through 175 responses to The Edge’s 2014 inquiry: What scientific idea is ready for retirement?, and selected a few responses offering very likely the best candidate: statistics.

Emanuel Derman, professor of financial engineering at Columbia, wrote that the power of statistics is an idea worth retiring:

    …nowadays the world, and especially the world of the social sciences, is increasingly in love with statistics and data science as a source of knowledge and truth itself. Some people have even claimed that computer-aided statistical analysis of patterns will replace our traditional methods of discovering the truth, not only in the social sciences and medicine, but in the natural sciences too.

    … Statistics—the field itself—is a kind of Caliban, sired somewhere on an island in the region between mathematics and the natural sciences. It is neither purely a language nor purely a science of the natural world, but rather a collection of techniques to be applied, I believe, to test hypotheses. Statistics in isolation can seek only to find past tendencies and correlations, and assume that they will persist. But in a famous unattributed phrase, correlation is not causation.

    Science is a battle to find causes and explanations amidst the confusion of data. Let us not get too enamored of data science, whose great triumphs so far are mainly in advertising and persuasion. Data alone has no voice. There is no “raw” data, as Kepler’s saga shows. Choosing what data to collect and how to think about it takes insight into the invisible; making good sense of the data collected requires the classic conservative methods: intuition, modeling, theorizing, and then, finally, statistics.

Science journalist Charles Seife wrote that “statistical significance” is almost invariably misused, to the point that it has become

    a boon for the mediocre and for the credulous, for the dishonest and for the merely incompetent. It turns a meaningless result into something publishable, transforms a waste of time and effort into the raw fuel of scientific careers. It was designed to help researchers distinguish a real effect from a statistical fluke, but it has become a quantitative justification for dressing nonsense up in the mantle of respectability. And it’s the single biggest reason that most of the scientific and medical literature isn’t worth the paper it’s written on.
6 Feedbacks on "“Lies, Damned Lies, and Statistics”"


on statistical significance:
The plain fact is that in 1925 Ronald Fisher gave scientists a mathematical machine for turning baloney into breakthroughs, and flukes into funding.
—Robert Matthews


Yes, we should take away the use of anything that can be used improperly by people with bad motives. Like statistics. And guns.


Statistics can give you insight to trends, underlying causation and clues to follow to real discovery. Statistics can be used to distort facts, prove a lie, create a scapegoat and decieve the gullible. It’s up to you the individual to know truth from lies; good statistical practice from fraud. Basic statistics shoud be taught in high school to everyone, not simply the nuts and bolts of statistical practice but the good and the bad and the ineffective and misuse techniques as well. Education and understanding of what is being taught is the best way to avoid becoming a victim of a statistics based fraud. Let’s face it, half of Americans are at or below average intelligence and 5 out of 4 Americans lack sufficient science and math background to detect the errors and outright fraud that out political driven science community is pushing on us.

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