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 BioStatistical Blog
 1. Statistic’s Dirty Little Secret
 1.A Another view on testing by Peter Flom, PhD
 1.B Am I a nattering nabob of negativism?
 2. Why do we compute pvalues?
 3. Meaningful ways to determine the adequacy of a treatment effect when you have an intuitive knowledge of the dependent variable
 4. Meaningful ways to determine the adequacy of a treatment effect when you lack an intuitive knowledge of the dependent variable
 5. Accepting the null hypothesis
 6. ‘Lies, Damned Lies, and Statistics’ part 1, and Analysis Plans, an essential tool
 7. Assumptions of Statistical Tests
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 Allen Fleishman on 22. A question on QoL, Percentage Change from Baseline, and CompassionateUsage Protocols
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 Allen Fleishman on 24. Simple, but SimpleMinded
 Victor Levenson on 24. Simple, but SimpleMinded
 Allen Fleishman on 24. Simple, but SimpleMinded
 Victor Levenson on 24. Simple, but SimpleMinded
 Allen Fleishman on 12. Significant pvalues in small samples
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 Allen Fleishman on 12. Significant pvalues in small samples
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Category Archives: Statistics
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
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
Posted in pvalues, Statistics, Treatment Effect
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2. Why do we compute pvalues?
In my previous blog I said that the pvalue, 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
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
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
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
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
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
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, pvalues, Power, Statistics
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10. Parametric or nonparametric 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
11. pvalues by the Pound
‘Failure is always an option’ – Myth Busters ‘The Statistician is in. 5¢ per pvalue.’ ‘Your statistical report is being delivered by three UPS trucks.’ ‘It takes three weeks to prepare a good adlib speech.’ – Mark Twain *** Let … Continue reading
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
22. A question on QoL, Percentage Change from Baseline, and CompassionateUsage 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
24. Simple, but SimpleMinded
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
29. Should you publish a nonsignificant 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, pvalues, Power, Psychology, Statistics, Treatment Effect
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BioStatistical 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 →