There's an abundance of well-known resources offering advice on data visualization. (E.g. Tufte, Stephen Few et al, Nathan Yau.) But to what field(s) might one turn to for answers to questions like these:

  • Is the pie chart criticism relevant in practice? Are people that much better at interpreting linear scale length than arc length?
  • Say I construct an index summary of a set of underlying variables, and explain to a lay audience that the United States has a 100 value in 2010, and a 110 in 2015. How will most people interpret these numbers? Are there natural cognitive habits that I should consider as I present this metric, either to leverage for better explanation or to guard against misinterpretation?

Put another way, to what scientific fields can presenters of quantitative information look for empirically sound and tested principles that help sort through the plethora of visualization and design advice available these days?

The aim is not to find advice, ideas or current consensus on how best to visualize data or approach novel data visualization problems, but to learn where to look for the science of how people interpret quantitative and/or visual information.

(Extra credit for references to journals, conferences and scholars of the field.)

  • 2
    $\begingroup$ Concerning the pie chart, this piece of priceonomics might interest you. Specifically the references to Cleveland and McGill and Robbins. $\endgroup$ Commented Nov 21, 2015 at 18:18
  • 1
    $\begingroup$ I highly recommend the IEEE VIS conference, ieeevis.org! $\endgroup$ Commented Jan 26, 2016 at 13:29
  • 1
    $\begingroup$ @LouisaGrey Thanks! I've been once, a good experience to be sure. $\endgroup$ Commented Feb 16, 2016 at 21:55

2 Answers 2


Gerd Gigerenzer is widely acknowledged as one of the world experts in the cognitive aspects of numeracy or, alternatively, innumeracy. He has many papers and books on these topics referenced on his website (https://www.mpib-berlin.mpg.de/en/staff/gerd-gigerenzer). One of his key texts is his 2002 book Calculated risks: How to know when numbers deceive you. Read the abstract here: https://www.mpib-berlin.mpg.de/en/research/adaptive-behavior-and-cognition/publications/books/calculated-risks

Related to Gigerenzer's work is cognition-based decision theoretic work that looks at the way information is presented. A representative paper here is Dan Goldstein's The Illusion of Wealth and its Reversal available here ... http://rady.ucsd.edu/docs/seminars/goldstein.pdf Here's from the intro:

Recently, researchers and policy makers have started to pay more attention not just to choice architecture but also to information architecture: the format in which information is presented to people. Research in information architecture has shown, for example, that the caloric content of food can be well appreciated in terms of the amount of exercise it would take to work calories off, and the comprehension of cars’ energy efficiency can be enhanced by presenting information in terms of gallons per 100 miles instead of miles per gallon. This paper investigates information architecture, though instead of consuming calories or gasoline, we address economic consumption in retirement.

An important recent addition to the literature is Berkeley Dietvorst's research into "algorithm aversion" and decision-making. Dietvorst contends that wrt predictive modeling, the technically naive and/or illiterate tend to assume that predictive models are a "magic bullet" or perfectly informative and when the algorithms prove to be, at best, weakly predictive, then the typical response is to reject quantitative solutions altogether.


Then there are bloggers like Kaiser Fung who maintains his Junkcharts website critiquing the graphs and visualizations of major pubs such as the NYTs or the WSJ http://junkcharts.typepad.com/

Related to your question of visualization is the work of design experts such as Manuel Lima who maintains a website VisualComplexity.com covering the many approaches to this. Lima also teaches data visualization at Parsons School of Design in NYC. http://www.visualcomplexity.com/vc/

Besides Parsons, other design and visualization institutions include:

College of Design and Social Context https://www.rmit.edu.au/about/our-education/academic-colleges/college-of-design-and-social-context/

UCLA's Culture Analytics Institute

Google's Cultural Institute https://www.google.com/culturalinstitute/home

A MoMA design exhibition and book



In terms of conferences there is the Eyeo Festival http://eyeofestival.com/

In R software, the visualization guru is Hadley Wickham http://had.co.nz/

In SAS software, there is Rob Allison http://www.robslink.com/SAS/graph_book.htm

Finally, there are no shortage of "one-off" kinds of websites:

http://infosthetics.com/ great visuals of govt data



How to display data badly by Karl Broman https://www.biostat.wisc.edu/~kbroman/presentations/IowaState2013/graphs_combined.pdf


Maria Popova's Design and Communication blog https://www.brainpickings.org/2012/06/26/talk-to-me-moma-paola-antonelli-book/

Gallery of Data Visualization http://www.datavis.ca/gallery/index.php

Periodic Table of Data Visualization http://www.visual-literacy.org/periodic_table/periodic_table.html

Our World in Data http://ourworldindata.org/

This just begins to scratch the surface of what's out there...


Psychophysics studies how humans respond to and interpret stimuli, to include interpretation of data visualizations. The Cleveland and McGill paper linked in the comments is an example, and the second section of this paper gives a quick overview of a few perspectives.

Numerical or mathematical cognition is a sub-discipline of cognitive science that studies things like number sense. It sometimes borrows concepts from psychophysics, for instance Fechner's scale, which "states that subjective sensation is proportional to the logarithm of the stimulus intensity." Wiki's description of the concept applied to numerical cognition:

Psychological studies show that it becomes increasingly difficult to discriminate among two numbers as the difference between them decreases. This is called the distance effect. This is important in areas of magnitude estimation, such as dealing with large scales and estimating distances. It may also play a role in explaining why consumers neglect to shop around to save a small percentage on a large purchase, but will shop around to save a large percentage on a small purchase which represents a much smaller absolute dollar amount.

Related, in behavioral economics, prospect theory (original paper) examines human choices between risky, probabilistic alternatives.


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