Category Archives: Biostatistics

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

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

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

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

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

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

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

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

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