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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
 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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Tag Archives: Biostatistics
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 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