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 BioStatistical Blog
 1. Statistic’s Dirty Little Secret
 1.A Another view on testing by Peter Flom, PhD
 1.B Am I a nattering nabob of negativism?
 2. Why do we compute pvalues?
 3. Meaningful ways to determine the adequacy of a treatment effect when you have an intuitive knowledge of the dependent variable
 4. Meaningful ways to determine the adequacy of a treatment effect when you lack an intuitive knowledge of the dependent variable
 5. Accepting the null hypothesis
 6. ‘Lies, Damned Lies, and Statistics’ part 1, and Analysis Plans, an essential tool
 7. Assumptions of Statistical Tests
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 Allen Fleishman on 24. Simple, but SimpleMinded
 Victor Levenson on 24. Simple, but SimpleMinded
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 Allen Fleishman on 12. Significant pvalues in small samples
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Category Archives: nonnormality
7. Assumptions of Statistical Tests
“All models are incorrect. Some are useful.” George Box *** When you do a statistical test, you are, in essence, testing if the assumptions are valid. We are typically only interested in one, the null hypothesis. That is, the assumption … Continue reading
7a. Assumptions of Statistical Tests: Ordinal Data
In my ‘7. Assumptions of Statistical Tests‘, there was a glaring omission (at least glaring for me): no discussion of ordinal data. I felt that I let my readers down, by glossing over this issue. Time to repair my quite … Continue reading
10. Parametric or nonparametric analysis – Why one is almost useless
… ‘If you lost your watch in that dark alley, why are we looking here?’ ‘Well, <hic> there’s light here.’ (old chestnut) *** In my last blog, I stated that we should avoid dichotomizing as it throws away a lot … Continue reading
18. Percentage Change from Baseline – Great or Poor?
Everything should be made as simple as possible, but no simpler. – Attributed to Albert Einstein *** In my third and forth blog I addressed useful ways to present the results of an analysis. Of course, pvalues wasn’t it. I … Continue reading
19. A Reconsideration of My Biases
No virtual observations were harmed in the running of this study. A man with one watch knows what time it is. A man with two is not sure, at least for the Wilcoxon test. And don’t try this at home, … Continue reading
Posted in ANOVA, Effect Size, nonnormality, nonparametric statistics, pvalues, Power, ttest
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