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 therapist? Go solve your own problems!”, anonymous student
“If I only had a day to live, I’d spend it in a statistics class. That way it would seem longer”, anonymous student.
A guy is flying in a hot air balloon and he’s lost. So he lowers himself over a field and shouts to a guy on the ground:
“Can you tell me where I am, and which way I’m headed?”
After fifteen minutes the guy on the grounds says, “Sure! You’re at 43 degrees, 12 minutes, 21.2 seconds north; 123 degrees, 8 minutes, 12.8 seconds west. You’re at 212 meters above sea level. Right now, you’re hovering, but on your way in here you were at a speed of 1.83 meters per second at 1.929 radians”
“Thanks! By the way, are you a statistician?”
“I am! But how did you know?”
“You took a long time to answer; everything you’ve told me is completely accurate; you gave me more detail than I needed, and you told me in such a way that it’s no use to me at all!”
“Dang! By the way, are you a principal investigator?”
“Geeze! How’d you know that????”
“You don’t know where you are, you don’t know where you’re going. You got where you are by blowing hot air, you start asking questions after you get into trouble, and you’re in exactly the same spot you were a few minutes ago, but now, somehow, it’s my fault!
“Like other occult techniques of divination, the statistical method has a private jargon deliberately contrived to obscure its methods from non-practitioners.” G. O. Ashley
Numbers are like people; torture them enough and they’ll tell you anything.
50% of all citizens of this country have a below average understanding of statistics
“Statistics: the mathematical theory of ignorance” Morris Kline
Statistical Analysis: Mysterious, sometimes bizarre, manipulations performed upon the collected data of an experiment in order to obscure the fact that the results have no generalizable meaning for humanity. Commonly, computers are used, lending an additional aura of unreality to the proceedings.
THE TOP NINE REASONS TO BECOME A STATISTICIAN
Deviation is considered normal.
We feel complete and sufficient. We are “mean” lovers.
Statisticians do it discretely and continuously.
We are right 95% of the time.
We can legally comment on someone’s posterior distribution.
We may not be normal but we are transformable.
We never have to say we are certain.
We are honestly significantly different.
No one wants our jobs.
A statistics professor was completing what he thought was a very inspiring lecture on the importance of significance testing in today’s world. A young nursing student in the front row sheepishly raised her hand and said, “But sir, why do nurses have to take statistics courses?”
The professor thought for a few seconds and replied, “Young lady, statistics save lives!”
The nursing student was utterly surprised and after a short pause restored, “But sir, please tell us how statistics saves lives!”
“Well,” the professor’s voice grew loud and somewhat angry, “STATISTICS KEEPS ALL THE IDIOTS OUT OF THE NURSING PROFESSION!!!”
Proofs? We don’t need no stinking proofs.
A colleague once told me of being confronted by a doctor at 4 pm on a Friday with “Could you just ‘t and p’ this data by Monday?” David Spiegelhalter, president of the Royal Statistical Society
These blogs were written with the non-statistician in mind, although statisticians could benefit from my thirty plus years of experience consulting for the pharmaceutical/biologic/device industry. It is for those people who have taken at least a single statistics class and use statistics for clinical research in the pharmaceutical/device/biotech industry. Although simple equations will be presented, to make points, math will be kept to the level of a first week in high school algebra. Nor will I present proofs.
I will be making postings on important issues for the users of statistics and insights I’ve made from my many years of experience. I’ll also include ‘tricks’ for running a smaller study. Please start at the bottom of this blog and read upward (starting with 1. Statistic’s dirty little secret).
Feel free to post your thoughts, agreeing or disagreeing (include why you disagree, please). I will post questions or statistically related agreements/disagreements. Interesting (either positively or negatively) comments might be the lead-in for a full post. However I will attempt to answer all comments within a day. [Note: I use a spam filter, so if your comment is ignored send it to me at my e-mail address allen-fleishman (at) comcast.net.] Feel free to ask me a question through a comment. However, I am no Dr. Phil. I will almost never say your approach was the correct one, especially with a typical 4 sentence description of your trial. Even given a well written protocol, I could never guess all possible data perturbations. So I will point out potential issues, most can be anticipated if you read the entire set of blogs.
Blogs I have written are (although I reserve the right to change the blogs and comments after they were initially published):
1. Statistic’s dirty little secret – Published 30Sept2011
1.A. Another View on Testing by Peter Flom, PhD – Published 12July2012
1.B. Am I a nattering nabob of negatisism? – Published 23April2017
2. Why do we compute p-values? – Published 5Oct2011
3. Meaningful ways to determine the adequacy of a treatment effect when you have an intuitive knowledge of the d.v. – Published 12Oct2011
4. Meaningful ways to determine the adequacy of a treatment effect when you lack an intuitive knowledge of the d.v. – Published 19Oct2011
5. Accepting the null hypothesis – Published 30Oct2011
5.A. Accepting the null hypothesis by Randy Gallisted, PhD of Rutgers – Published 23Apr2012
6. ‘Lies, Damned Lies and Statistics’ part 1, and Analysis Plan (an essential tool) – Published 5Nov2011
7. Assumptions of Statistical Tests – Published 11Nov2011
7a. Assumptions of Statistical Test: Ordinal Data – Published 2Aug2012
8. What is a Power Analysis? – Published 28Nov2011
9. Dichotomization as a devil’s tool – Published 10Dec2011
10. Parametric or non-parametric analysis – Why one is almost useless – Published 26Dec2011
11. p-values by the pound – Published 5Jan2012
12. Significant p-values in small samples – Published 25Jan2012
13. Multiple observations and Statistical ‘Cheapies’ – Published 12Mar2012
14. Great and Not so Great Designs – Published 22Mar2012
15. Variance, and t-tests, and ANOVA, oh my! – Published 9Apr2012
16. Comparing many means – Analysis of VARIANCE? – Published 7May2012
17. Statistical Freebies/Cheapies – Multiple Comparisons and Adaptive Trials without self-immolation – Published 21May2012
18. Percentage Change from Baseline – Great or Poor? – Published 4Jun2012
19. A Reconsideration of my Biases – Published 25Jun2012
20. Graphs I: A Picture is Worth a Thousand Words – Published 17Aug2012
21. Graphs II: The Worst and Better Graphs – Published 18Sept2012
22. A question on QoL, Percentage Change from Baseline, and Compassionate-Usage Protocols – Published 20Apr2013
23. Small N study, to Publish or not – Published 12May2014
24. Simple, but Simple Minded – Published 8Aug2014
25. Psychology I: A Science – Published 20Mar2015
26. Psychology II: A Totally Different Paradigm -Published 25Mar2015
27. Number of Events from Week x to Week y – Published 7Apr2015
18.1 Percentage Change – A Right Way and a Wrong Way – Published 28Aug 2015
28. Failure to Reject the Null Hypothesis – Published 7Nov 2015
29. Should you publish a non-significant result? – Published 22Nov2015
18.2 Percentage Change Revisited – Published 9March2016
30. ‘Natural’ Herbs and Alternative Medicine – Published 25July2016
31. Case History of a Trial – To be Done
32. Randomizations (also a comment to the Democratic National Committee)