In article <002b01c1eab3$b79dc420$a54bfea9@p0h1e7>,
Voltolini <[EMAIL PROTECTED]> wrote:
>Dear friends,
>How to choose between parametric and non parametric tests? Following any
>texbook we can find the idea of using histograms, curtosis, skewness and...
>tests like Shapiro-Wilk to test normality. In the case of homocedasticity,
>we can use one of the best for this task, the Levene test.
WHY are you testing for normality? I would not believe it no
matter how much evidence was presented. The same holds for
the other restrictive conditions commonly tested for.
There are two issues here; one is that the problem is not
whether the null hypothesis is true, but whether one should
act as if it is. The question is not whether there is no
difference between the treatments; there is a difference.
The question is whether the difference is small enough that
the simplicity gain outweighs the loss due to the difference.
Also, many procedures originally based on normality work
quite well without it. For regression, linearity is the
important assumption, and homoscedasticity is of some
importance, but normality only somewhat affects significance
levels, which are not by themselves important, anyhow.
>But..... how can we check the test assumptions using other tests with
>assumptions too? Any statistical test is a probability model and any model
>need some assumptions to be accepted! For example, does anyone know what are
>the assumptions of the Shapiro-Wilk and Levene tests? How to test them?
This is one of the bad features of almost all statistics texts.
They do not start out with the assumptions, and with them, how
important are the various assumptions.
>I am a biologist teaching statistics and trying to teach more than formulas
>for my students, so... please help me with this discussion !
>Voltolini
If you, and your students, want to understand statistics,
do not start them out with ANY statistical procedures
until they understand probability (NOT how to calculate
probabilities) as well as evaluation of consequences and
probability modeling.
Evaluation of the consequences is not easy, but it is the
only reasonable way of selecting procedures and making
sound decisions.
It is necessary to simultaneously consider
all consequences of the proposed action in
all states of nature.
--
This address is for information only. I do not claim that these views
are those of the Statistics Department or of Purdue University.
Herman Rubin, Dept. of Statistics, Purdue Univ., West Lafayette IN47907-1399
[EMAIL PROTECTED] Phone: (765)494-6054 FAX: (765)494-0558
.
.
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