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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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Stat comments and Questions
 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
 Merm on 12. Significant pvalues in small samples
 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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Category Archives: Treatment Effect
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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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
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
12. Significant pvalues 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 subsections is for Statistical Consultants. A short time ago, there were over fifty comments on the topic of ‘Does … Continue reading
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
Posted in ANOVA, Treatment Effect, Variance
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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
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
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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