Re: Hot hands, continued .....
- 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
> 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
> Anyway, Merry Christmas to everyone!!