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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
 Phil Assheton on 12. Significant pvalues in small samples
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Category Archives: Power
8. What is a Power Analysis?
People who haven’t the time to do things ‘perfectly’, always have time to do them over. Measure once, cut twice. Measure twice, cut once. ‘My god, you’ve conclusively proven it. Time equals Money.’ *** Cost for Failure Every CEO I’ve … Continue reading
9. Dichotomization as the Devils Tool
There are two types of people, those who classify people into two types of people and those who don’t. Never trust anyone over thirty. As Mason said to Dixon, ‘you gotta draw the line somewhere’. *** Don’t get me wrong, … Continue reading
Posted in Biostatistics, Dichotomization, Interval, Nominal, Ordinal, pvalues, Power, Statistics
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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
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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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
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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29. Should you publish a nonsignificant result?
53. If the beautiful princess that I capture says “I’ll never marry you! Never, do you hear me, NEVER!!!”, I will say “Oh well” and kill her. 61. If my advisors ask “Why are you risking everything on such a … Continue reading
Posted in Effect Size, pvalues, Power, Psychology, Statistics, Treatment Effect
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