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st: re: conflicting tests for normality

From   Kouji Asakura <>
To   statalist digest <>
Subject   st: re: conflicting tests for normality
Date   Fri, 25 Feb 2011 07:34:04 -0800 (PST)

Thanks for your informative responses. I'll bear these things in mind for my 
Koji Asakura

In addition, note that there need be no contradiction here. For example, a 
distribution might be approximately symmetric but have fatter tails than the 
normal, or asymmetric but happen to have about the same kurtosis as a normal.  
Furthermore, it is rare that the marginal normality of a variable is quite what 
you should be worried about. Even when normality is an assumption, it is usually 
that responses are conditionally normal given predictors, or equivalently that 
disturbances are normal, and that's usually the least important assumption being 
made, although for bizarre reasons it's often the assumption that is most 
scrutinised.  Nick  On Fri, Feb 25, 2011 at 2:10 PM, Maarten buis 
<> wrote: > --- On Fri, 25/2/11, Kouji Asakura wrote: >> 
I need help with a problem I'm having. I'm testing for >> normality of a 
variable and I made use of the tests in >> Stata;  Shapiro-Wilk, the sktest, and 
Shapiro-Francia. >> However, I obtained conflicting results. > <snip> >> So you 
see, the -sktest- says it's not normal, while both >> Shapiro tests say the 
opposite, at least at a 0.05 alpha. > > This is a rather difuse hypothesis: 
there are many ways in > which a distribution can deviate from a theoretical > 
distribution. This makes it a hard hypothesis to test, and > often leads to not 
very powerful tests. So it is no surprise > that different tests give different 
outcomes. > > The first thing I would do is graph the distribution and > see to 
what extend and, more importantly, in what way the > distribution deviates from 
normality/Gaussianity. Two > useful graphs for this purpose are -qnorm- and 
-hangroot-, > whereby the latter is user written and can be downloaded > by 
typing in Stata -ssc install hangroot-. > > Once you have figured out how the 
distribution deviates > from normality/Gaussianity, you can make an informed > 
decision on whether you want to do something about it, > and if so, what. This 
is just another way of saying that > you need to know what the problem is before 
you can think > about how to fix it.

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