Category Archives: assumptions

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

Posted in assumptions, Biostatistics, heteroscedasticity, non-normality, non-parametric statistics, Statistics, Uncategorized | 14 Comments

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

Posted in ANOVA, assumptions, Biostatistics, non-normality, non-parametric statistics, Ordinal, Statistics | 2 Comments

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

Posted in assumptions, Biostatistics, Dichotomization, Effect Size, heteroscedasticity, Interval, non-normality, non-parametric statistics, Ordinal, p-values, Power, Statistical, Statistics | 2 Comments

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

Posted in assumptions, Biostatistics, SAP, Statistical Analysis Plan, Statistics, Uncategorized | Leave a comment

12. Significant p-values 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 sub-sections is for Statistical Consultants.  A short time ago, there were over fifty comments on the topic of ‘Does … Continue reading

Posted in assumptions, Confidence intervals, Effect Size, p-values, Treatment Effect | 83 Comments

18. Percentage Change from Baseline – Great or Poor?

Everything should be made as simple as possible, but no simpler. – Attributed to Albert Einstein *** In my third and forth blog I addressed useful ways to present the results of an analysis.  Of course, p-values wasn’t it.  I … Continue reading

Posted in Analysis of Covariance, assumptions, Effect Size, heteroscedasticity, non-normality, percentage change from baseline | 18 Comments

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

Posted in assumptions, Confidence intervals, Design, Effect Size, non-parametric statistics, Ordinal, p-values, Parallel Group, Power, t-test, Treatment Effect | 1 Comment

24. Simple, but Simple-Minded

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

Posted in assumptions, Design, percentage change from baseline, Statistics, Treatment Effect | 4 Comments

28. Failure to Reject the null hypothesis

Let me again start with a truism, Failure to reject the null hypothesis is not the same as accepting it.  One can ONLY reject the null hypothesis. To many, failure to reject the null hypothesis is equivalent to saying that the difference … Continue reading

Posted in Accepting the Null hypothesis, assumptions, Power | Leave a comment