Agreed, you must understand the likely origins of outliers. One example -
published share price time series may not take account of share
splits/merges, which will cause the reported price to increase or decrease
from the time of the event; and certain classes of investment may be
re-denominated from pence to pounds, or from one currency to another. I
know of a number of occasions when trading systems have been thrown off
kilter by data scrubbing algorithms that discarded legitimate data as
outliers.

On 10 January 2012 14:38, Donna Y <[email protected]> wrote:

> There are lots of possibletechniques for discarding outliers.  The
> important thing is to know about the reason for their ocurence.  Is it
> caused by some type of error in the generation or collection of the data or
> is it actually important information.  You might rather concentrate on the
> outliers exactly because they deviate from the norm and have the potential
> of to effect quite different from normal behavior.
>
> Donna
> [email protected]
>
>
> On 2012-01-09, at 7:49 PM, Roger Hui <[email protected]> wrote:
>
> > I wonder if there are well-known techniques in statistics for dealing
> with
> > the following problem.
> >
> >      t
> > 11 10 10 10 10 11 10 10 10 10 9 11 10 11 10 10 11 10 11 10 11 10 10
> >      11 10 11 10 10 10 11 10 74 11 11 14 11 11 10 12 11 15 14 12 11
> >      11 11 11 11 10 12 11 11 11 10 11 11 11 10 11 11 10 11 161241 49
> >      32 12 11 11 12 10 11 10 12 11 12 11 11 12 11 11 12 11 11 11 12
> >      11 11 12 11 11 11 11 11 11 11 10 11 11 12 12
> >
> > t is a set of samples from a noisy source which is supposed to give the
> > same integer answer.  Obviously, 161241 is an "outlier", and it is likely
> > that 74, 49, or even 32 are outliers too.  Are there standard techniques
> > for discarding outliers to clean up the data, before the application of
> > statistical tests such as the means test or large sample test?
> > ----------------------------------------------------------------------
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> >
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