Category Archives: Statistical

Bio-Statistical Blog

I have retired from consulting.  However I will post blogs on statistics when I see interesting materials to comment on. Allen I. Fleishman, PhD, PStat® Did you hear the joke about statisticians? … Probably “Professor, do I look like a … Continue reading

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1. Statistic’s Dirty Little Secret

I can’t believe schools are still teaching kids about the null hypothesis.  I remember reading a big study that conclusively disproved it years ago. *** To most scientists, the endpoint of a research study is achieving the mystical ‘p < 0.05’, … Continue reading

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2. Why do we compute p-values?

In my previous blog I said that the p-value, which test the null hypothesis, is a near meaningless concept.  This was based on: In nature, the likelihood that the difference between two different treatments will be exactly any number (e.g., … Continue reading

Posted in Biostatistics, Confidence intervals, Statistical, Statistics, two-sided test, Uncategorized | Tagged , , , , , , | Leave a comment

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

6. ‘Lies, Damned Lies, and Statistics’ part 1, and Analysis Plans, an essential tool

Lies, Damn Lies, and Statistics Let me start this blog with one of my pet peeves.  I abhor the quote ‘Lies, Damn Lies, and Statistics’.  For me, a statistician, it has as much truth as saying that ‘the earth is … Continue reading

Posted in Analysis Plan, Biostatistics, SAP, Statistical, Statistical Analysis Plan, Statistics | 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