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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 24. Simple, but SimpleMinded
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 Allen Fleishman on 24. Simple, but SimpleMinded
 Victor Levenson on 24. Simple, but SimpleMinded
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 Allen Fleishman on 12. Significant pvalues in small samples
 Phil Assheton on 12. Significant pvalues in small samples
 Allen Fleishman on 12. Significant pvalues in small samples
 Omar Farooque on 12. Significant pvalues in small samples
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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
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 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
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 pvalue actually has, but explained in a second blog why we still do it. I gave in my last blog a 1to1 alternative for the pvalue, and why we need to … 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
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
15. Variance, and ttests, 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, pvalues, ttest, Uncategorized, Variance
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5.A Accepting the NullHypothesis 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
Posted in Biostatistics, Uncategorized
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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
Posted in Design, Uncategorized
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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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31. Case History of a Trial
A colleague asked me to review a trial. I will mask the identity of the trial and obfuscate irrelevant details, like the disease, timing, treatment, and key parameter. The patients had abnormal parathyroid glans, with hypercalcemia. This was a Phase … Continue reading