> The absolute
> value of e grows as L grows, but by how much? It seems statistical
> theory claims it grow by an order of the square root of L.
Assuming you want the standard deviation for the number of successes,
given p=0.5:
#exact
0.5 * sqrt (n)
#numerical approximation
sd (rbinom (1e6, n,
+ (in addition to Jeff's link)
https://en.wikipedia.org/wiki/Binomial_distribution
Bert Gunter
"The trouble with having an open mind is that people keep coming along and
sticking things into it."
-- Opus (aka Berkeley Breathed in his "Bloom County" comic strip )
On Sat, Aug 22, 2020 at 6:50 AM
stats.stackexchange.com
On August 21, 2020 1:25:06 PM PDT, Wayne Harris via R-help
wrote:
>
>I'm intested in understanding why the standard error grows with respect
>to the square root of the sample size. For instance, using an honest
>coin and flipping it L times, the expected number of HEADS
I'm intested in understanding why the standard error grows with respect
to the square root of the sample size. For instance, using an honest
coin and flipping it L times, the expected number of HEADS is half and
we may define the error (relative to the expected number) to be
e = H - L/2,
whe
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