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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) -

Kaiser

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.

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