Main Site
Search

Recent Posts
 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
Categories
Stat comments and Questions
 Allen Fleishman on 22. A question on QoL, Percentage Change from Baseline, and CompassionateUsage Protocols
 Kate on 22. A question on QoL, Percentage Change from Baseline, and CompassionateUsage Protocols
 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
RSS Feeds
Category Archives: Confidence intervals
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
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
Posted in Biostatistics, Confidence intervals, NonInferiority, noninferiority hypothesis, noninferiority hypothesis, Not worse than
Tagged confidence intervals, inferiority hypothesis, inferiority testing, noninferiority, noninferiority, not worse than, proving the null hypothesis
Leave a comment
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
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
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