On 6 March 2014 06:45, Gabriel Bodeen <gabebod...@gmail.com> wrote:

> Brent was right but the explanation could use some examples to show you
> what's happening.  The strangeness that you noticed occurs because you're
> looking at cases where the proportion is *exactly* 50%.
>
> binopdf(2,4,0.5)=0.375
> binopdf(3,6,0.5)=0.3125
> binopdf(4,8,0.5)=0.2374
> binopdf(8,16,0.5)=0.1964
> binopdf(1000,2000,0.5)=0.0178
> binopdf(1e6,2e6,0.5)=0.0006
>
> Instead let's look at cases which are in some range close to 50%.
>
> binocdf(5,8,0.5)-binocdf(3,8,0.5)=0.4922
> binocdf(10,16,0.5)-binocdf(6,16,0.5)=0.6677
> binocdf(520,1000,0.5)-binocdf(480,1000,0.5)=0.7939
> binocdf(1001000,2e6,0.5)-binocdf(999000,2e6,0.5)=0.8427
> binocdf(1000050000,2e9,0.5)-binocdf(999950000,2e9,0.5)=0.9747
>
> Basically, as you flip a coin more and more times, you get a growing
> number of distinct proportions of heads and tails that can come up, so any
> exact proportion becomes less likely.  But at the same time, as you flip
> the coin more and more times, the distribution of proportions starts to
> cluster more and more tightly around the expected value.  So for tests when
> you do two million flips of a fair coin, only about 0.06% of the tests come
> up exactly 50% heads and 50% tails, but 84.27% of the tests come up between
> 49.95% and 50.05%.
>

Thank you, that's exactly what I was attempting to say in my cack-handed
way. (And it is almost certainly what Max intended to say.)

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