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Little Data: How traditional statistical ideas remain relevant in a big-data world

See if you can interpolate the talk from the slides.

The background is: I was invited to speak in this seminar on “big data.” I said I didn’t know anything about big data, I worked on little data. They said that was ok. Actually it was probably a crowd-pleasing move to tell these people that little-data ideas remain relevant.


  1. Martin Romero says:

    Sun Tzu said:
    The control of a large force
    is the same principle as the control of a few men:
    it is merely a question of dividing up their numbers.
    Perhaps, if he were statistician, we could have said :
    The analysis of a large data
    is the same principle as the analyisis of a small data:
    it is merely a question of dividing up hierarchically their variables .

  2. John Mashey says:

    Interesting in multiple ways.


    ‘PEAR closed its doors at the end of February 2007 with its founder, Robert G. Jahn, concluding that after tens of millions of trials they had demonstrated that human intention has a slight effect on random-event machines.[8] “For 28 years, we’ve done what we wanted to do, and there’s no reason to stay and generate more of the same data,”[1] Jahn said. Jahn felt that the work showed, on average, people can shift 2–3 events out of 10,000 from chance expectations.[8]

    2) In the 1990s, I did a lot of “big data” talks, but sadly, did not try to trademark the term.:-) See “Origins?” here.