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The fascinating website edge.org, which publishes well-considered reflections by smart people on very big questions, has made this its 2014 inquiry: What scientific idea is ready for retirement?
The respondents are well worth listening to: they include scientists of many stripes, mathematicians, philosophers, and economists, as well as several knowledgeable science writers and editors (and also, for reasons that are obscure, Alan Alda). The responses are posted one after another in a gigantic stream, forming a kind of alpha-listicle. It’s Buzzfeed for boffins, essentially.
A ton of big scientific ideas get voted off the island on the respondents’ page (see sampling below), but there is one response in particular that I thought might be of interest to our little subset, concerned as we are with things like elections. 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.
Bart Kosko, an information scientist and EE and law professor at USC, responded similarly that statistical independence is an illusion:
It is time for science to retire the fiction of statistical independence.
The world is massively interconnected through causal chains. Gravity alone causally connects all objects with mass. The world is even more massively correlated with itself. It is a truism that statistical correlation does not imply causality. But it is a mathematical fact that statistical independence implies no correlation at all. None. Yet events routinely correlate with one another. The whole focus of most big-data algorithms is to uncover just such correlations in ever larger data sets.
Statistical independence also underlies most modern statistical sampling techniques. It is often part of the very definition of a random sample. It underlies the old-school confidence intervals used in political polls and in some medical studies. It even underlies the distribution-free bootstraps or simulated data sets that increasingly replace those old-school techniques.
White noise is what statistical independence should sound like… Real noise samples are not independent. They correlate to some degree.
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.
What say you, Ricochetti? First, what’s your take on the ever-growing popular reverence for statistics? And second, what scientific idea do you believe ought to be retired? To get your gears turning, here’s the promised selection of ideas the respondents came up with at edge.org (and check out the site; there are lots more where these came from):
- Mental illness is nothing but brain illness (Joel Gold and Ian Gold)
- Animal mindlessness (Kate Jeffery)
- Altruism (Jamil Zaki and Tor Norretranders)
- Entropy (Bruce Parker)
- Moral “blank slate-ism” (Kiley Hamlin)
- Natural selection is the only engine of evolution (Athena Vouloumanos)
- Opposites can’t both be right (Eldar Shafir)
- Left brain/right brain (Sarah-Jayne Blakemore and Stephen M. Kosslyn)
- The self (Bruce Hood)
- Beauty is in the eyes of the beholder (David M. Buss)
- String theory (Frank Tipler)
- Emotion is peripheral (Brian Knutson)
- Unification, or a theory of everything (Marcelo Gleiser, Geoffrey West)
- The intrinsic beauty and elegance of mathematics allows it to describe nature (Gregory Benford)
- Cause and effect (W. Daniel Hillis)
- Evidence-based medicine (Gary Klein)
- There can be no science of art (Jonathan Gottschall)
- Mouse models (Azra Raza, MD)
- The clinician’s law of parsimony (aka Occam’s Razor) (Gerald Smallberg, Jonathan Haidt)
- Standard deviation (Nassim Nicholas Taleb)
- Artificial intelligence (Roger Schank)
- Human nature (Peter Richerson)
- Programmers must have a background in calculus (Andrew Lih)
- Free will (Jerry Coyne)
- The Big Bang was the first moment of time (Lee Smolin)
- One genome per individual (Eric J. Topol, MD)
- Languages condition worldviews (John McWhorter)
- Infinity (Max Tegmark)