On Feb 21, 2009, at 9:47 PM, LuKreme wrote:

> so in this example case, say I wanted 95% certainty, I would then plug
> the number into the formula you gave, like this:
>
> p* = (21/80) =.2625

Well, actually it should be 21/82 but you get the idea.
>
>
> .2625 ± [ sqrt(.2625 * .7375 / 82) * 1.9599 ]
>
> .2625 ± 0.095229752 = 0.167270248 <-> 0.357729752
>
> So, I am 95% certain that the true probability is in the range of
> 16.72% to 35.77% or so.  So, my 'sense' that 19/78 is pretty close to
> 25% is pretty much shit.
>
> If we move up and order of magnitude we get
>
> .2625 ± [ sqrt(.2625 * .7325 / 792 ) * 1.9599 ]
>
> .2625 ± 0.030537944
>
> Still a 3% range even after nearly 800 trials.
>
> OK, that at least tells me that my 'sense' is completely worthless in
> this case.  What I have to do is decide the confidence interval I
> want, and then calculate the possible range of values, which is
> exactly opposite of what I was thinking (calculate the value, and then
> calculate a ± confidence on that value).

You have grasped it. The  sampling distribution of a proportion is a  
bell shaped curve but a wider and flatter one than maybe you would  
intuit. Also, to cut the confidence interval in half you must  
quadruple the sample size so you run into a sort of  diminishing  
returns to getting more data.

I probably should have mentioned that you need to have a big enough  
sample that you have at least 5 successes and 5 failures before you  
can conclude anything.
---
Neither a man nor a crowd nor a nation can be trusted to act humanely  
or to think sanely under the influence of a great fear.

-Bertrand Russell, philosopher, mathematician, author, Nobel laureate  
(1872-1970)


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