I think this is the time when I would like to ACTUALLY understand what you are 
talking about...

I think this is important, but you lost me on nimimi:
> N!/M!*M!

Would appreciate any examples from personal-life-perspecitve too!



> On 9 Mar 2020, at 22:08, Bruce Kellett <[email protected]> wrote:
> 
> On Tue, Mar 10, 2020 at 8:54 AM Russell Standish <[email protected] 
> <mailto:[email protected]>> wrote:
> On Sun, Mar 08, 2020 at 10:10:23PM +1100, Bruce Kellett wrote:
> > 
> >     >     > In order to infer a probability of p = 0.5, your branch data 
> > must
> >     have
> >     >     > approximately equal numbers of zeros and ones. The number of
> >     branches
> >     >     with
> >     >     > equal numbers of zeros and ones is given by the binomial
> >     coefficient. For
> >     >     large
> >     >     > even N = 2M trials, this coefficient is N!/M!*M!. Using the
> >     Stirling
> >     >     > approximation to the factorial for large N, this goes as 
> > 2^N/sqrt
> >     (N)
> >     >     (within
> >     >     > factors of order one). Since there are 2^N sequences, the
> >     proportion with
> >     >     n_0 =
> >     >     > n_1 vanishes as 1/sqrt(N) for N large.
> > 
> > 
> > 
> > This is the nub of the proof you wanted.
> 
> No - it is simply irrelevant. The statement I made was about the
> proportion of strings whose bit ratio lies within certain percentage
> of the expected value.
> 
> After all when making a measurement, you are are interested in the
> value and its error bounds, eg 10mm +/- 0.1%, or 10mm +/- 0.01mm. We
> can never know its exact value.
> 
> 
> If you are using experimental data to estimate a quantity (and a p value is a 
> quantity in the required sense), then you are interested in the confidence 
> interval, not an absolute or percentage error. And the confidence interval 
> for a given probability of including the true value decreases with the number 
> of trials (since the standard error decreases with N).
> 
> Bruce
> 
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