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

Posted in Biostatistics, Confidence intervals, Effect Size, Omega Square, Statistical, Statistics, Treatment Effect | Leave a comment

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

Posted in Biostatistics, Dichotomization, Effect Size, Key Comparison, Non-Inferiority, non-inferiority hypothesis, non-parametric statistics, noninferiority hypothesis, p-values, Power, Statistical, Statistics, Treatment Effect | Leave a comment

10. Parametric or non-parametric 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

Posted in assumptions, Biostatistics, Dichotomization, Effect Size, heteroscedasticity, Interval, non-normality, non-parametric statistics, Ordinal, p-values, Power, Statistical, Statistics | 2 Comments

12. Significant p-values 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 sub-sections is for Statistical Consultants.  A short time ago, there were over fifty comments on the topic of ‘Does … Continue reading

Posted in assumptions, Confidence intervals, Effect Size, p-values, Treatment Effect | 83 Comments

14. Great and Not so Great Designs

Common Designs to Reduce Error Variance Contra-lateral 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

Posted in Analysis of Covariance, Contra-lateral, Crossover, Design, Effect Size, Parallel Group | Leave a comment

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, p-values wasn’t it.  I … Continue reading

Posted in Analysis of Covariance, assumptions, Effect Size, heteroscedasticity, non-normality, percentage change from baseline | 18 Comments

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, non-normality, non-parametric statistics, p-values, Power, t-test | 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

Posted in assumptions, Confidence intervals, Design, Effect Size, non-parametric statistics, Ordinal, p-values, Parallel Group, Power, t-test, Treatment Effect | 1 Comment

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 non-significant 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

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