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

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

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, p-values, Power, Statistics | 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

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

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 | Leave a comment

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

Posted in Effect Size, p-values, Power, Psychology, Statistics, Treatment Effect | Leave a comment