## Re: [HACKERS] Standard Deviation function.

From: Tom Lane hackers(at)postgreSQL(dot)org Re: [HACKERS] Standard Deviation function. 1998-06-05 15:24:04 28715.897060244@sss.pgh.pa.us (view raw or whole thread) 1998-06-05 15:24:04 from Tom Lane pgsql-hackers
```Andreas Zeugswetter <andreas(dot)zeugswetter(at)telecom(dot)at> writes:
> Wow, this is it. But as I said, the above line is wrong (By the way:
> this is a very common mistake).
> It should read:
> 	\$self->{standard_deviation}  = sqrt( \$self->{pseudo_variance} / \$self->{count} )
> Note: The - 1 is missing

The formula with N-1 in the divisor is correct for the "sample standard
deviation".  That is what you use when your N data points represent a
sample from a larger population, and you want to estimate the standard
deviation of the whole population.

If your N data points in fact are the *whole* population of interest,
then you calculate the "population standard deviation" which has just N
in the divisor.  So both versions of the formula are correct depending
on the situation, and you really ought to provide both.

(To justify the difference intuitively: if you have exactly one data
point, and it is the *whole* population, then the mean equals the
data value and the standard deviation is zero.  That is what you get
with N in the divisor.  But if your one data point is a sample from
a larger population, you cannot estimate the population's standard
deviation; you need more data.  The N-1 equation gives 0/0 in this
case, correctly signifying that the value is indeterminate.)

I think the Perl code given earlier in the thread pretty much sucks
from a numerical accuracy point of view.  The running mean calculation
suffers from accumulation of errors, and that propagates into the
pseudo-variance in a big way.  It's particularly bad if the data is
tightly clustered about the mean; the code ends up doing lots of
subtractions of nearly equal values.

The accepted way to do sample standard deviation in one pass is this:

STDDEV = SQRT( (N*SIGMA(Xi^2) - SIGMA(Xi)^2) / (N*(N-1)) )

where N is the number of data points and SIGMA(Xi) means the sum
of the data values Xi.  You keep running sums of Xi and Xi^2 as
you pass over the data, then you apply the above equation once
at the end.  (For population standard deviation, you use N^2 as
the denominator.  For variance, you just leave off the SQRT().)

All that you need to implement this is room to keep two running
sums instead of one.  I haven't looked at pgsql's aggregate functions,
but I'd hope that the working state can be a struct not just a
single number.

regards, tom lane

```

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