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# Re: st: sign test output

 From Nahla Betelmal To statalist@hsphsun2.harvard.edu Subject Re: st: sign test output Date Thu, 17 Jan 2013 13:13:22 +0000

```Again, thank you both for your comments.

However, if normality test is proved to be useful only for huge sample
as Maarten mentioned. How can we determine which test (i.e parametric
or non-parametric ) to be used for smaller sample size in hundreds?!

I personally think it is irrational to run both t-test and sign test
on the same sample and hope they both produce the same conclusion! and
what if they dont!

I will follow Nick's advise to look deeper in the data, but I still
believe that there must be another way to give obvious solution to
this situation.

Thank you both again, I highly appreciate your kind help and time,

Nahla

On 17 January 2013 12:22, Nick Cox <njcoxstata@gmail.com> wrote:
> The row boat [English English: rowing boat] joke is as least as old as
> a comment in
>
> Box. G. E. P. 1953. Non-normality and tests on variances. Biometrika 40: 318-35
>
> which is otherwise germane to the discussion in several ways, not
> least in introducing the term "robustness".
>
> Nick
>
> On Thu, Jan 17, 2013 at 12:14 PM, Maarten Buis <maartenlbuis@gmail.com> wrote:
>> On Thu, Jan 17, 2013 at 11:21 AM, Nahla Betelmal wrote:
>>> from my readings in statistics , I know that in order to decide
>>> whether to use parametric or non-parametric tests, the data normality
>>> distribution should be checked first.
>>>
>>>  Shapiro-Wilk is used to test normality, when the number of
>>> observations is less than 30. Otherwise, we should use
>>> Kolmogorov-Smirnov for large sample (as in my sample).
>>
>> Unfortunately that is incorrect. Normality tests need huge samples
>> before the p-value means what it is supposed to mean. An analogy I
>> have heard in a different context, but which applies to this situation
>> very well is: to go out to sea in a row boat to check whether the sea
>> is safe for the QE II.
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```