|
Your tennis ball example has nothing to do with the central limit theorem, it simply illustrates that precision of a statistical estimate (standard error) improves (in quadrature) with the sample size.
Let's say you were trying to estimate the skyglow of your image, and so you sampled an area of 'background' sky and recorded the mean pixel intensity. That would be one estimate, and its precision would depend on how many pixels constituted your sample (this is akin to your tennis ball example). Then, if you did that over repeated images, the distribution of those estimates would be Gaussian, irrespective of the underlying distribution of the pixels within the sampling area (might be uniform, lognormal, whatever). THAT is the central limit theorem.
|