Category Archives: p-values

1.A Another view on testing by Peter Flom, PhD

The following was written by Peter Flom, PhD dated November 4, 2009 from a Book Review: Statistics as Principled Argument by Robert Abelson.  His website is His blog is  I changed the date of publication of 12July2012 to 1Oct2011 … Continue reading

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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

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

15. Variance, and t-tests, and ANOVA, oh my!

Statistics – a specialty of mathematics whose basic tenant is ‘Exceptions prove the rule’. Mr. McGuire: I just want to say one word to you. Just one word. Benjamin: Yes, sir. Mr. McGuire: Are you listening? Benjamin: Yes, I am. … Continue reading

Posted in ANOVA, p-values, t-test, Uncategorized, Variance | Leave a comment

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

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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