« Going bananas over this Seamless ad | Main | It's not us; it's the weather »


Feed You can follow this conversation by subscribing to the comment feed for this post.

Jose Antonio Sobrino Reineke

thank you for this post, I am also struggling with this issue. You mention "big data" here, perhaps just for the click value of the term, but this problem has been true from long before the rise of "big data" as a buzzword. the problem is not with machines or machine learning, but rather the human expectation that complicated explanations are better. There is a book from the 1970s, How Real is Real, by Paul Watzlawick, that described some interesting experiments with people classifying slides of biopsies of cancer cells. Simple and correct theories were discarded in favor of complicate but erroneous theories. I would refer you to the citation for more details when i find it.
BTW, the beginning of your post is somewhat difficult to understand. Just after citing the Lewis book (which I have here open in front of me), your intro to the "vignette" is confusing. Would suggest that you make clear that test subjects are presented with either (A) or (B) -


JASR: Tackling your last point first, I presented one version of the experiment in which two groups were presented (A) or (B). What is even more shocking is that when both (A) and (B) were presented to the same subjects, they still adjusted their predictions in the same way! In Lewis's book, I believe he disclosed this additional insight when describing a different experiment - and cited Kahneman as saying that it was Tversky's idea to present both statements to the same subjects, and he didn't believe people would still make the error - but they did!

The Big Data connection is intentional and very real. In that world, adding more data frequently means adding more variables not increasing sample size. And machines are supposed to overcome human errors. The point of the title is that this type of error has been recognized since the 1970s, long before Big Data, but it is relevant today.

The comments to this entry are closed.

Get new posts by email:
Kaiser Fung. Business analytics and data visualization expert. Author and Speaker.
Visit my website. Follow my Twitter. See my articles at Daily Beast, 538, HBR, Wired.

See my Youtube and Flickr.


  • only in Big Data
Numbers Rule Your World:
Amazon - Barnes&Noble

Amazon - Barnes&Noble

Junk Charts Blog

Link to junkcharts

Graphics design by Amanda Lee

Next Events

Jan: 10 NYPL Data Science Careers Talk, New York, NY

Past Events

Aug: 15 NYPL Analytics Resume Review Workshop, New York, NY

Apr: 2 Data Visualization Seminar, Pasadena, CA

Mar: 30 ASA DataFest, New York, NY

See more here

Principal Analytics Prep

Link to Principal Analytics Prep