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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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Stat comments and Questions
 Allen Fleishman on 22. A question on QoL, Percentage Change from Baseline, and CompassionateUsage Protocols
 Kate on 22. A question on QoL, Percentage Change from Baseline, and CompassionateUsage Protocols
 Allen Fleishman on 24. Simple, but SimpleMinded
 Victor Levenson on 24. Simple, but SimpleMinded
 Allen Fleishman on 24. Simple, but SimpleMinded
 Victor Levenson on 24. Simple, but SimpleMinded
 Allen Fleishman on 12. Significant pvalues in small samples
 Merm on 12. Significant pvalues in small samples
 Allen Fleishman on 12. Significant pvalues in small samples
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Category Archives: assumptions
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
11. pvalues by the Pound
‘Failure is always an option’ – Myth Busters ‘The Statistician is in. 5¢ per pvalue.’ ‘Your statistical report is being delivered by three UPS trucks.’ ‘It takes three weeks to prepare a good adlib speech.’ – Mark Twain *** Let … Continue reading
12. Significant pvalues in small samples
‘Take two, they’re small’ *** Are the results from small, but statistically significant, studies credible? One of the American Statistical Association’s subsections is for Statistical Consultants. A short time ago, there were over fifty comments on the topic of ‘Does … 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
23. Small N study to publish or not?
The following question was sent to me, I thought it useful enough for a full elaboration: Submitted on 2014/05/12 at 8:23 am Dr. Fleishman, I am so happy I found your site. I have been trying to decide how to best … Continue reading
24. Simple, but SimpleMinded
Science is organized common sense where many a beautiful theory was killed by an ugly fact. – Thomas Huxley It really is a nice theory. The only defect I can think it has is probably common to all philosophical theories. … Continue reading
28. Failure to Reject the null hypothesis
Let me again start with a truism, Failure to reject the null hypothesis is not the same as accepting it. One can ONLY reject the null hypothesis. To many, failure to reject the null hypothesis is equivalent to saying that the difference … Continue reading
Posted in Accepting the Null hypothesis, assumptions, Power
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