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Re: st: sign test output
From
William Buchanan <[email protected]>
To
"[email protected]" <[email protected]>
Subject
Re: st: sign test output
Date
Thu, 17 Jan 2013 05:51:48 -0800
The determination of whether to use parametric vs non-parametric tests is based on the assumptions you bring to the data. Relying solely on any one, or several, statistical tests can be useful, but it should not be your sole criteria. Nick's suggestion for visually inspecting your data will provide you with much more information about the distributions and characteristics of your data.
HTH,
Billy
Sent from my iPhone
On Jan 17, 2013, at 5:13, Nahla Betelmal <[email protected]> wrote:
> 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 <[email protected]> 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 <[email protected]> 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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