3,065 reputation
1133
bio website biomath.ugent.be/biomath/…
location Ghent, Belgium
age 37
visits member for 4 years, 2 months
seen Nov 17 at 15:42

Statistician and R programmer at the faculty of Bio-Engineering, university of Ghent

Co-author of 'R for Dummies' (out in july 2012 )

contact : Joris - dot - Meys - at - Ugent - dot - be


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comment Change confidence risk of chisq
Feel free to edit. I did my best, but don't know how to call "random variation" like my grandmother understands it when talking to other statisticians. Randomness is part of the model, as in the random error. But randomness is also part of the experimental setup, as random selection from the population. Hence, a non-random effect in a model can be caused by random variation nonetheless.
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revised Change confidence risk of chisq
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Dec
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comment Change confidence risk of chisq
@whuber Tried to use wording that's understood by people with a limited knowledge of statistics. Feel free to clarify, I would honestly not know how to clarify it. Continuous struggle with the students here as well...
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reviewed Approve suggested edit on Change confidence risk of chisq
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answered Change confidence risk of chisq
Nov
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revised How to calculate estimated proportions and their confidence intervals from a mixed model?
edited title
Nov
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answered How to calculate estimated proportions and their confidence intervals from a mixed model?
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awarded  Good Question
Nov
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comment Is normality testing 'essentially useless'?
@whuber You're free to update :) otherwise I'll update it when I find a bit more time. Cheers.
Oct
25
comment Is normality testing 'essentially useless'?
Btw, QQ plots are not meant to detect such mixtures. They're graphical tools that give you a fair idea about whether or not you'll lose power an even get biased estimates when using specific tests. That's all there is to them. For 99% of the statistical questions in practical science, that's more than enough.
Oct
25
comment Is normality testing 'essentially useless'?
Not one real life distribution is perfectly normal. So with large enough samples, all normality test should reject the null. So yes, SW does what it needs to do. But it is worthless for applied statistics. There's no point in going to eg a Wilcoxon when having a sample size of 5000 and an almost normal distribution. And that's what OP's remark was all about: does it make sense to test for normality when having large sample sizes? Answer: no. Why? because you detect (correctly) a deviation that doesn't matter for your analysis. As pointed out by the QQ plots
Oct
25
comment Is normality testing 'essentially useless'?
That fact is true, but has no bearance with the CLT. The CLT is pretty specific about under what conditions the approximation holds. You throw different things on the same heap. Yes, Wilcox gives those examples. No, he isn't talking about large sample sizes or dismissing the CLT, far from even. He rightfully points out people forget about the conditions under which the CLT holds. I agree with you that testing differences with a sample size of 5000 doesn't make sense without stating what the minimal relevant difference is. But that's a whole other issue.