For years I’ve been working on changing my thinking–even when just musing about nothing in particular–from “I wonder whether…” to “I wonder to what extent…”. It has taken a while, but now I usually do find myself thinking in terms of “How big is the effect of…?” rather than “Is there an effect of…?”
I worked on making that change, despite decades of immersion in NHST, because I’ve long felt that overcoming dichotomous thinking has to be at the heart of improving how we do statistics. No more mere black-white, sig-nonsig categorisation of findings!
The discontinuous mind?
In UTNS I wrote:
“Why does dichotomous thinking persist? One reason may be an inherent preference for certainty. Evolutionary biologist Richard Dawkins (2004) argues that humans often seek the reassurance of an either-or classification. He calls this ‘the tyranny of the discontinuous mind’. Computer scientist and philosopher Kees van Deemter (2010) refers to the ‘false clarity’ of a definite decision or classification that humans clutch at, even when the situation is uncertain. To adopt the new statistics we may need to overcome an inbuilt preference for certainty, but our reward could be a better appreciation of the uncertainty inherent in our data.” (pp. 8-9)
Now there’s an article (Fisher & Keil, 2018) reporting evidence that such a binary bias may indeed be widespread:
It’s behind a paywall unfortch, but here’s the abstract
One of the mind’s most fundamental tasks is interpreting incoming data and weighing the value of new evidence. Across a wide variety of contexts, we show that when summarizing evidence, people exhibit a binary bias: a tendency to impose categorical distinctions on continuous data. Evidence is compressed into discrete bins, and the difference between categories forms the summary judgment. The binary bias distorts belief formation—such that when people aggregate conflicting scientific reports, they attend to valence and inaccurately weight the extremity of the evidence. The same effect occurs when people interpret popular forms of data visualization, and it cannot be explained by other statistical features of the stimuli. This effect is not confined to explicit statistical estimates; it also influences how people use data to make health, financial, and public-policy decisions. These studies (N = 1,851) support a new framework for understanding information integration across a wide variety of contexts.
Fisher and Keil reported multiple studies using a variety of tasks. All participants were from the U.S. and recruited via Mechanical Turk. Therefore, an unknown proportion, but perhaps a low proportion, would have had some familiarity with NHST, so the evidence for a binary bias probably does not reflect an influence of NHST–in accord with the authors’ claim that their results support a binary bias as a general human characteristic.
Surmounting our “discontinuous mind” (thank you Richard Dawkins) could, ideally, lead to us finding “that p values become kind of vestigial…”, as Bob wrote in his recent detailed and cogent post in reply to Lakens.
Bring that on!
Dawkins, R. (2004). The ancestor’s tale: A pilgrimage to the dawn of life. London: Weidenfeld & Nicolson.
Fisher, M., & Keil, F. C. (2018). The binary bias: A systematic distortion in the integration of information. Psychological Science, 29, 1846-1858.
van Deemter, K. (2010). Not exactly: In praise of vagueness. Oxford: Oxford University Press.