Author Archives: Allen Fleishman

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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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 http://www.statisticalanalysisconsulting.com/ His blog is http://www.statisticalanalysisconsulting.com/blog/  I changed the date of publication of 12July2012 to 1Oct2011 … Continue reading

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1.B Am I a nattering nabob of negativism?

This blog was written on April 23, 2017, but was ‘published on October 2, 2011, so it appears after blog 1.A  Another view on testing by Peter Flom, PhD. I wrote my first blog, ‘1. Statistic’s dirty little secret‘, in September … 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

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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

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4. Meaningful ways to determine the adequacy of a treatment effect when you lack an intuitive knowledge of the dependent variable

In previous blogs I discussed how little relevance the p-value actually has, but explained in a second blog why we still do it.  I gave in my last blog a 1-to-1 alternative for the p-value, and why we need to … Continue reading

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5. Accepting the null hypothesis

“All models are wrong.  … But some are useful.” George Box *** In my first blog I stated a truism, that was hopefully taught in your first statistics class: You can’t accept the null hypothesis.  You can ONLY reject the … Continue reading

Posted in Biostatistics, Confidence intervals, Non-Inferiority, non-inferiority hypothesis, noninferiority hypothesis, Not worse than | Tagged , , , , , , | 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

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7. Assumptions of Statistical Tests

“All models are incorrect.  Some are useful.” George Box *** When you do a statistical test, you are, in essence, testing if the assumptions are valid.  We are typically only interested in one, the null hypothesis.  That is, the assumption … Continue reading

Posted in assumptions, Biostatistics, heteroscedasticity, non-normality, non-parametric statistics, Statistics, Uncategorized | 14 Comments

7a. Assumptions of Statistical Tests: Ordinal Data

In my ‘7. Assumptions of Statistical Tests‘, there was a glaring omission (at least glaring for me): no discussion of ordinal data.  I felt that I let my readers down, by glossing over this issue.  Time to repair my quite … Continue reading

Posted in ANOVA, assumptions, Biostatistics, non-normality, non-parametric statistics, Ordinal, Statistics | 2 Comments

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

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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

11. p-values by the Pound

‘Failure is always an option’ – Myth Busters ‘The Statistician is in.  5¢ per p-value.’ ‘Your statistical report is being delivered by three UPS trucks.’ ‘It takes three weeks to prepare a good ad-lib speech.’ – Mark Twain *** Let … Continue reading

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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

13. Multiple Observations and Statistical ‘Cheapies’

‘Measure once cut twice, measure twice cut once’ ‘A man with one watch knows what time it is, a man with two is never sure’ The three words which get everyone’s attention:  Free, Free, Free *** Multiple observations occur in … Continue reading

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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

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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

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5.A Accepting the Null-Hypothesis a Bayesian approach

The following blog was written by Randy Gallistel, PhD of Rutgers.  It presents a Bayesian approach to hypothesis testing.  It was written on April 23, 2012, but will eventually appear to have an earlier date, to sort it immediately after … Continue reading

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16. Comparing many means – Analysis of VARIANCE?

What do you call a numbers cruncher who is creative?  An accountant. What do you call a numbers cruncher who is uncreative?  A statistician. *** In the last blog, I explored the meaning of variance.  I said that variance is … Continue reading

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17. Statistical Freebies/Cheapies – Multiple Comparisons and Adaptive Trials without self-immolation

If you shoot enough arrows, everyone can hit a bull’s-eye Free!   Free!  Free! BOGO – Buy One Get One Free *** Multiple Comparison Problem:  If you shoot at a target once and hit the bull’s-eye, then you’ve clearly hit your … Continue reading

Posted in Adaptive Trial, Interim Analysis, Key Comparison, Multiple Comparisons | 2 Comments

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

20. Graphs I: A Picture is Worth a Thousand Words

A picture is worth a thousand words Everything should be made as simple as possible, but no simpler. C’est la Bérézina – French phrase meaning ‘it’s a complete disaster’ *** We’ve all heard it, ‘A picture is worth a thousand … Continue reading

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21. Graphs II: The Worst and Better Graphs

At times, I strive to be an educated Simpleton. *** OK, we know that a great graph can Sing!!!  But do we know anything about what conveys information the best?  Yes.  Many psychology studies have been done to tell us … Continue reading

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22. A question on QoL, Percentage Change from Baseline, and Compassionate-Usage Protocols

Yesterday was 1 degree Fahrenheit and today is 10.  I’m ten times warmer!! Compassion?  We statisticians have evolved beyond such petty human affects. I received the following question from Simon Wilkinson from New Zealand:  Dear Allen. To set the scene, … Continue reading

Posted in Confidence intervals, Design, Interval, Nominal, Ordinal, percentage change from baseline, Statistics, Treatment Effect | 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

24. Simple, but Simple-Minded

Science is organized common sense where many a beautiful theory was killed by an ugly fact. – Thomas Huxley It really is a nice theory.  The only defect I can think it has is probably common to all philosophical theories. … Continue reading

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25. Psychology I – A Science?

“Psychology is a crock”, Bob Newhart show, Season 4 Episode 8 *** Let me start out with a detail I don’t publicize much.  My PhD was in Quantitative Psychology or Psychometrics.  Like Biometrics, it is the mathematical end of the … Continue reading

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26. Psychology II: A Totally Different Pardigm

Simple Truths: If you want to study the most complex system in the world, do not rely on the simplest tools/methodology or mathematics a fifth grader could apply. If you want to study an individual, you need to study an individual. If … Continue reading

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27. Number of Events from Week x to Week y

What is your right shoe size?  What is your left shoe size? How many horses?  Simple, you count the number of hooves and divide by four. A man with one watch knows what time it is.  A man with two … Continue reading

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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

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

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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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18.2 Percentage Change Revisited

If you hear hoof beats, the first thing you should NOT look for is unicorns. I’d like to thank Rob Musterer, President of ER Squared, for posting a reference to a 2009 paper by Ling Zhang and Han Kun, ‘How … Continue reading

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30. ‘Natural’ Herbs and Alternative Medicines

Just b’cause it ain’t science, don’t mean it ‘taint so. Phrenology, Four humors, You were cursed, Leeches, … ************************************ This blog is written for the general public, not for the pharma/device expert who is my typical target for my blog. My … Continue reading

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31. Burdon of Proof, Useful Data, Reliability, Radiological Diagnosis, and Iridology

The eyes are the windows to the soul. “If you cannot measure it, it does not exist.” Young psychometrician Hmm, lost an eye?  My professional opinion is to cover your good eye with gauze so you can only see light or … Continue reading

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