- Thread starter Anatomist
- Start date
- Tags mann-whitney test post-hoc adjustment spss

Where does this statement comes from? Has it been written in a textbook, or where?

One of the assumptions of parametric tests is that the data are normally distributed.

framework of the general linear model, for example,

the model residuals or data within GROUPS should

preferabily be normally distributed, not the unconditional

data.

sample size is below 10 (which is my case)

perhaps do a short research about Bonferroni correction

and then pick your pocket calculator to perform the

correction. By the way, if your number of tests is

large, Bonferroni correction could be very conservative

i.e. it may then be nearly impossble to obtain significant

results.

With kind regards

K.

Best regards,

A.

Post-hoc testing is done e.g. when an omnibus test

with multiple groups turned out significant and

then pairwise comparisons are performed (this

is the case for example with parametric ANOVA

or a Kruskal-Wallis H-test, both with k > 2 groups).

What you seemingly did was multiple testing

(several U-tests), for which protection against

inflation of type 1 error risk might be sought.

With kind regards

K.

One of the assumptions of parametric tests is that the data are normally distributed.

Some of the parametric test are based on the normal distribution.

But there are many other parametric distributions. Like the Poisson distribution, with a parameter that is often called "lambda". Or the binomial distribution with parmeters "p" and "n". Or the exponential distribution...

One usual test is to do likelihood ratio tests.

However, if the sample size is below 10 (which is my case), then the normality tests are rather useless (*Biometry* by Sokal & Rohlf; *Biostatistics* by Zar).

But since the OP says that, how can the OP know that his(or her) data is not normally distributed?

One should remember that the Student t-test and F-tests in analysis of variance were designed to be small sample test. Student himself used a sample size of four (n=4) in his original paper from 1908 (page 13).

But a non-parametric tests like Mann-Whitney has assumptions too, like being continuous variables, so no ties, and being sensitive to "spread" and skewness...

I collected my samples from museum specimens all over the world; so they do not even belong to the same population.

I have no idea if SPSS has a correction for post hoc test. You seem to know your basics. However, what K was gettting at was per the description of your data we don't get why you need to run post hoc tests, which are typically reserved to address pairwise comparison to combat multiplicity. From your description you don't seem to be running these. Also, since you don't seem to be running a KW test, which would typically be the cue to run more tests.

Why do you need post hoc tests?