This is getting OT, as I'm not making any comment on numpy's implementation, but...
yogesh karpate wrote: > # As far as normalization by(n) is concerned then its common assumption > that the population is normally distributed and population size is > fairly large enough to fit the normal distribution. But this standard > deviation, when applied to a small population, tends to be too low > therefore it is called as biased. OK. > # The correction known as bessel correction is there for small sample > size std. deviation. i.e. normalization by (n-1). but why only small size -- the "beauty" of the approach is that the "-1" makes less and less difference the larger n gets. > " . Its shown that for N=16 the std. deviation normalization was (n-1)=15 > # While I was learning statistics in my course Instructor would advise > to take n=20 for normalization by (n-1) Which introduces an incontinuity -- I never like incontinuities -- why bother? for large n, it makes no practical difference, for small n you want the -1 -- why arbitrarily decide what "small" is? From an engineering/applied science point of view, I take the view expressed in the Wikipedia page on Unbiased estimation of standard deviation: "...the task has little relevance to applications of statistics..." -Chris -- Christopher Barker, Ph.D. Oceanographer Emergency Response Division NOAA/NOS/OR&R (206) 526-6959 voice 7600 Sand Point Way NE (206) 526-6329 fax Seattle, WA 98115 (206) 526-6317 main reception chris.bar...@noaa.gov _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
