[Numpy-discussion] Standard Deviation (std): Suggested change for "ddof" default value

josef.pktd at gmail.com josef.pktd at gmail.com
Fri Apr 4 09:04:05 EDT 2014

On Fri, Apr 4, 2014 at 8:50 AM, Daπid <davidmenhur at gmail.com> wrote:
> On 2 April 2014 16:06, Sturla Molden <sturla.molden at gmail.com> wrote:
>> <josef.pktd at gmail.com> wrote:
>> > pandas came later and thought ddof=1 is worth more than consistency.
>> Pandas is a data analysis package. NumPy is a numerical array package.
>> I think ddof=1 is justified for Pandas, for consistency with statistical
>> software (SPSS et al.)
>> For NumPy, there are many computational tasks where the Bessel correction
>> is not wanted, so providing a uncorrected result is the correct thing to
>> do. NumPy should be a low-level array library that does very little magic.
> All this discussion reminds me of the book "Numerical Recipes":
> "if the difference between N and N − 1 ever matters to you, then you
> are probably up to no good anyway — e.g., trying to substantiate a
> questionable
> hypothesis with marginal data."
> For any reasonably sized data set, it is a correction in the second
> significant figure.

I fully agree, but sometimes you don't have much choice.

`big data` == `statistics with negative degrees of freedom` ?

or maybe

`machine learning` == `statistics with negative degrees of freedom` ?


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