Observational Studies

  • Lyrics © Mary McLellan
    may sing to the tune of Pharell William's "Happy"
     

    It might seem crazy what I’m ‘bout to say
    There was a big fire last saturday
    Hot coals and plumes of smoke everywhere
    They sent - they sent a lot of… Firemen in there

    Lurking variables make it look like there’s a link
    Lurking variables make it look like there’s a link
    Lurking variables make it look like there’s a link
    Lurking variables make it look like there’s a link

    All over the news about this and that
    There’s too much damage and you can’t go back
    If you send a lot of firemen to help you
    Firemen will cause the damage, it’s what they do!

    Lurking variables make it look like there’s a link
    Lurking variables make it look like there’s a link
    Lurking variables make it look like there’s a link
    Lurking variables make it look like there’s a link

  • Lyrics © Mary McLellan
    may sing to the tune of "99 Bottles"

    99 variables in the experiment, 99 variables
    Confounding variables cloud the issue, you can’t claim a cause
    98 variables in the experiment, 98 variables
    Confounding variables cloud the issue, you can’t claim a cause
    You can’t claim a cause, you can’t claim a cause

  • Lyrics © Mary McLellan 
    may sing to the tune of Queen's "Another One Bites the Dust"

    Simpson’s Paradox, it does not rock
    The conclusion you get from the separate groups
    Are different when combined
    Hey! Why does it do that?
    They added those fractions wrong

    Repeat

  • TigerSAMPLING is almost identical to TigerSTAT. However in the TigerSAMPLING game there are additional questions that emphasize BIAS and GENERALIZABILITY. These games collect data and explore models for estimating the age of a Siberian tiger. In this game, students act as researchers on a national preserve where they are expected to catch tigers, collect data, analyze their data (using simple linear regression on transformed data), and draw appropriate conclusions

  • ... if you can’t distinguish a solid statistical argument from a slick but bogus one, you’ll be an easy mark for disingenuous ideologues and hucksterish policy entrepreneurs of all stripes.

    Kathleen Geier (1963 - )

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