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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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Category Archives: Effect Size
3. Meaningful ways to determine the adequacy of a treatment effect when you have an intuitive knowledge of the dependent variable
“A theory has only the alternative of being wrong. A model has a third possibility – it might be right but irrelevant.” Manfred Eigen In my last stats course I was amazed to hear my teacher announce that If we … Continue reading
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
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
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
14. Great and Not so Great Designs
Common Designs to Reduce Error Variance Contralateral Design This is a very sweet type of study design, but it can be seldom used in pharma or biotech. Devices are another story. With only one group (rather than two) and the … 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
2 Comments
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
18.1 Percentage Change – Right Way and a Wrong Way
Eat s**t, 300 trillion flies can’t be wrong. [old joke punchline] “No, I dropped them in that dark alley, but I’d never find them there. That’s why we’re looking under the light post.” *** I came across a recent rant … Continue reading
Posted in Effect Size, percentage change from baseline
4 Comments
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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