Hapgood

Mike Caulfield's latest web incarnation. Networked Learning, Open Education, and Online Digital Literacy

  • Gallup 1946

    I knew about the poll in 1936 that changed everything — where the two million responses collated by the Literary Digest were dead wrong while the 50,000 responses scientifically selected by George Gallup were right. If you need a Wikipedia refresher on that, here you go: In 1936, [Gallup’s] new organization achieved national recognition by… Continue reading

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  • Problems of Definition: Elsevier’s Prices

    The recent boycott of Elsevier provides us with a great quote for use in a statistical literacy class. People are boycotting for a number of reasons, particularly because of the high cost of the “bundles” Elsevier sells. Claiming that their journals are some of the cheapest in the industry, an Elsevier rep states: “Over the past 10… Continue reading

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  • Skewness

    Skewness — I think the idea a distribution has a shape is something that some students just don’t grasp, and I’ve never got a good grip on what it is that blocks them from understanding concepts like skew (they get outliers at least in the broad, conversational sense, but skew remains a mystery). The weirdest… Continue reading

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  • Ecological Validity

    Term of the day: ecological validity. Ecological validity is a pretty big concern in ed psych, obviously. But I’ve also just read an interesting paper in Health, Risk, and Vulnerability which talks about the ecological validity of psychiatric assessment of criminals being treated for mental illness. The idea there is that many prisoners that do poorly in… Continue reading

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  • Simpson’s Paradox

    Example of Simpson’s Paradox from The Numbers behind Numb3rs. In this example„ women are accepted at a higher rate (or roughly equal rate) to all of Berkeley’s programs, but are accepted a a lower rate when those acceptances are combined into university-wide stats. Why? Because women apply to more competitive programs… Continue reading

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  • A good example of age as confounder

    From The Numbers behind Numb3rs: Cobb illustrated the distinction by means of a famous example from the long struggle physicians and scientists had in overcoming the powerful tobacco lobby to convince governments and the public that cigarette smoking causes lung cancer. Table 2 shows the mortality rates for three categories of people: nonsmokers, cigarette smokers,… Continue reading

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  • Predictive Efficiency

    From Farrington & Tarling’s Prediction in Criminology, a new term: predictive efficiency. The way to think about it is this — suppose I say that a college education predicts low incidence of being convicted of a violent crime, and at the end of the day I’m right — over the course of a year, 97.5%… Continue reading

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  • Incidence, Prevalence, and the Obama Job Record

    Since the statistics class I teach is supposed to be integrative — that is, to show connections between various disciplines and other aspects of life — I’m always on the lookout for ways to jury-rig an understanding from one domain to understand another. I think I just found a neat example. But first, look at… Continue reading

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  • From Swing Voters via ilovecharts This is a great example for students of how longitudinal measurement is sometimes used in polling to understand the effect of a specific event. The post-speech numbers alone tell us a bit about Obama’s popularity, but nothing about the speech. With a pre/post on the speech, we can use the post-speech gain to… Continue reading

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  • Diagnostic vs. Spectral Markers. From Principles of Medical Statistics. Diagnostic markers are about whether the disease is present, whereas spectral markers deal with severity and stage. Continue reading

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