1. The data on walking while drunk are staggering.
    2. The data on swimwear fashions are revealing.
    3. The data on LED bulbs are enlightening.
    4. The data on trout fishing are streaming.
    5. ​The data on herding horses were wrangled.
    6. The data on excavations were mined.
    7. The data on feedback are suggestive.
    8. The data on laundromats are cleaned.
    9. The data on the presidential debates were not convincing.
    10. The data on embossers are impressive.
    11. The data on cardiac transplants are disheartening. 
    12. The data on neuroses are worrisome.
    13. The data on chiropractors are aligned.
    14. The data on towing services were broken dow​n.
    15. The data on windshields are clear.
    16. The data on numismatics are collected.
    17. The data on grift are corrupted.
    18. The data on undergarments are supportive.

    Pedagogical guidance: provide your students with a couple of the above examples of this type of joke and ask them to invent their own jokes of the form

    "The data on _____A_________ are _____B_____,"

    and then to explain what it means for data to be ___B____.


    Larry Lesser and Dennis Pearl

  • by Lawrence Mark Lesser

    Statistics must be fast. . .
    ‘cause my newspaper gives numerical nuggets as “fast facts”
    ‘cause my doctor’s orders say STAT when she wants a test done now
    ‘cause velocity is a big feature of big data

    ‘cause some statisticians
    do accelerated testing,
    fast Fourier transforms,
    or a Tukey’s quick test

    ‘cause statisticians
    run studies,
    run simulations,
    and run tests (like a runs test)

    and those who run are fast, even
    in the long run

  • by Gill Marjorie Onate and Muzaffar Bhatti
    University of Toronto Mississauga

    Five, lucky, randomly chosen married couples.
    They were pink elephants, may I add,
    Were invited to the party of a lifetime,
    They were good looking, not too bad.

    But one couple looked different,
    Shorter than all the rest.
    Everyone called them “the outliers,”
    Even so, they looked their best!

    There may be a deviation from normality,
    Hmm… In what way?
    Only five lucky couples were chosen,
    Therefore, a small sample size and “the outliers,” I would say!
    The effect may be bigger. Should we rely on The Central Limit Theorem? 
    No way!

    Along the path, they walked and walked,
    Facing a fork up ahead.
    “Which way should we go?” They talked and talked,
    One elephant looked at the sign and read…

    “To make it to the Wilcoxon Signed Rank Test Party,
    You must pass this riddle:
    Does height have over 20 possible values???
    Pick quick, or you’re stuck in the middle.”

    Left is “Yes” and right is “No”
    “Hmm.. what’s the answer though?”

    Pink elephants come in all different sizes!
    Sizes come in all!
    Big or small,
    Short or tall!
     Going left would be the right call!

    Another sign at the fork up ahead,
    “Oh boy, I’m nervous,” one said.

    “Are the samples paired???
    Yes or No?”
    Of course they’re paired!
    They’re all married, can’t you see?
    “Let’s follow the ‘Yes’ path! It leads us to the Wilcoxon Signed Rank Test Party!” 
    “Yup, I agree!”

    “Just curious, 
    What would have happened if we went down the “No” path?” one asked.
    “It means we’re not paired, silly!
    At the end of that path is the Wilcoxon Rank Sum Test.
    It’s for the ones who are single
    And ready to mingle!”

    After long walks, 
    And after long talks,
    They made it, finally!
    Look at all the clocks!
    It’s time! Get ready!

    The countdown is starting,
    “Woohoo! This is the best!”