it's most easily explained for sigma known where xbar is within two sigma
of mu
95% of the time.  first put in mu with a two sigma band.  Next put in a
xbar with its confidence interval at both extremes of the band where it
JUST includes mu and within which xbar must be 95% of them time

On Mon, 18 Nov 2002, Alan McLean wrote:

> But how do you explain that you win 95% of the time?
>
>
> On Monday, November 18, 2002, at 11:10 AM, Elliot Cramer wrote:
>
> > Alan McLean <[EMAIL PROTECTED]> wrote:
> >
> > : The use of the t distribution in inference on the mean is on the
> > whole
> > : straightforward; my question relates to the theory underlying this
> > use.
> > : If Z = (X - mu)/sigma is ~ N(0, 1), then is T = (X - mu)/s (where s
> > is
> > : the sample SD based on a simple random sample of size n) ~ t(n-1)?
> > YES
> >
> > : My second question is on the matter of confidence intervals. In my
> >
> > : Whatever is said in the text books, this is understood by most
> > people as
> > : a statement that "mu lies in the interval with probability 0.95" - or
> > : something very close to this. In effect, we define a secondary
> > notional
> > : variable Y which imagines that we could find out the 'true' value of
> > mu;
> > : Y = 1 if this true value is in the confidence interval, = 0
> > otherwise -
> > : and we estimate the probability that Y = 1 as 0.95.
> >
> >
> > : So my question is: how do YOU explain to students what a confidence
> > : interval REALLY is?
> >
> > I treat it as a bet where on repeated samples I bet that mu is in the
> > region.  I win 95% of the time
> >
> >
> > ..
> > ..
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