# Thread: Normal versus non-normal - presentation of results in publication

1. ## Normal versus non-normal - presentation of results in publication

Dear all

Can anyone help me with the following question:
I have a outcome variable that is compared for different grouping variables. For some of the grouping variables the outcome looks in all groups normally distributed and hence I would go with parametric test. But there is one variable, if I take that one, the distribution in the corresponding groups does not look normal anymore. I wonder if in a publication for the same variables I should be consistent with the analysis method or if it is ok if once I use non-parametric and another time parametric approach.
As an example: Let the outcome be blood pressure. Blood pressure for females and males would be normally distributed => To test for difference between gender a t-test could be used. Blood pressure according to a certain co-morbidity (Having the co-morbity yes versus no) does not look normally distributed in the groups. Should I use for the same outcome (blood pressure) different methods or be consistent and always use the same method for the outcome blood pressure.

Lisa

2. ## Re: Normal versus non-normal - presentation of results in publication

Whether utilization of a "nonparametric" test is recommended, depends
largely on the sample size, which unfortunately was not mentioned here.
The usual approaches from the general linear model, such als ANOVA
or t-test or multiple regression analysis work quite fine with "non-normal"
distributions within groups, or of the residual, respectively, if sample size
is large enough (the central limit theorem probably applies already with
n > 30).

With kind regards

K.

3. ## Re: Normal versus non-normal - presentation of results in publication

Yes, that's true that with large sample size it should not be a problem.
But what would recommend in case of a small sample size? Or in another case where we want to test for equality of variances, where the violation of the normality would be problematic for the F-Test (and the same situation in some groups the variable is normal in others not).

Best regards
Lisa

4. ## Re: Normal versus non-normal - presentation of results in publication

But what would recommend in case of a small sample size?
Why don't you just tell us the figure?
Or in another case where we want to test for equality of variances, where the violation of the normality would be problematic for the F-Test (and the same situation in some groups the variable is normal in others not).
http://en.wikipedia.org/wiki/F-test_...ces#Properties

With kind regards

K.

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