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Re: Hot hands, continued .....

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  • Dean Oliver
    Without getting into too much here -- I haven t read the link mentioned here --- but usually the burden is to prove that nonstationarity exists (that there are
    Message 1 of 2 , Dec 30, 2003
      Without getting into too much here -- I haven't read the link
      mentioned here --- but usually the burden is to prove that
      nonstationarity exists (that there are different fg% vs time). I
      think Tversky's tests were basically testing that hypothesis and they
      couldn't rule out the null -- that stationarity existed (a constant

      But I do need to read the link.

      First, I need to sleep.


      --- In APBR_analysis@yahoogroups.com, "jsm_44092" <tpr42345@a...> wrote:
      > >Also, Tversky would probably have won the Nobel Prize had he not
      > >died. He definitely knew his stuff. That doesn't mean he's right
      > >about this, but it means that it would take a lot of work (not just
      > >anecdotes) to prove he's wrong (numerous other people have tried to
      > >do so and haven't done it).
      > Actually, Tversky's research on this was rather flawed, as pointed
      > out by Bob Wardrop in his technical report (thanks for the link, Ed).
      > I know Bob as our offices were side-by-side when I was a visiting
      > faculty member at the U. of Wisconsin in 1981-82.
      > There are many ways to go wrong using statistics, and I would expect
      > a non-statistician like Tversky to have some missteps. (Even the
      > renowned statistician Sir Maurice Kendall had an embarrassing
      > oversight when he was the 3rd author of a paper about 35 years ago.)
      > I don't have time to read all of Bob's TR, but I've read enough to be
      > able to understand what he is talking about. He discussed
      > autocorrelation and nonstationarity, in particular. Distinguishing
      > between the two can be tricky unless here is a large amount of data,
      > and both can exist in a set of data.
      > In case anyone is unfamiliar with these terms, I'll use a
      > illustration or two. Let's say you are a quality control supervisor
      > and one of your inspectors reports a defective unit on the first unit
      > of production (assume these are large units that are produced
      > slowly). Are you going to assume that the probability that the next
      > unit of production is equal to the long-term proportion, or are
      > you going to assume that the probability is greater. If you make the
      > first assumption, you are rejecting the possibility of
      > autocorrelation or nonstationarity. If the make the second
      > assumption, you are assuming that either or both of the two exist.
      > Think about field goal kickers. It is well-known that their job is
      > largely mental. If they miss a short-to-moderate length field goal,
      > there is a good chance they will miss the next one. Billy Bennett of
      > the U. of Ga., a great kicker, had this problem in one game this
      > year, as did Luke Manget of Ga. Tech in a stretch of games last year.
      > (Manget is 2nd on the all-time NCAA Division IA list for most
      > consecutive extra points.)
      > Nonstationarity is when there is a change in the average of what is
      > being measured, and Bob in his experiment using his former student
      > was convinced that her shooting performance exhibited nonstationarity.
      > This could be due to a number of factors: a biorhythm effect,
      > worrying over a term paper, etc.
      > Similarly, if an industrial process is out of control and an
      > increased proportion of defective units are produced, this is
      > nonstationarity.
      > Autocorrelation and/or nonstationarity certainly exists in sports, so
      > we can't automatically assume Bernoulli trials, as researchers are
      > inclined to do. Needless to say, this makes the analysis much more
      > complicated.
      > Anyway, Merry Christmas to everyone!!
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