http://www.bmj.com/content/309/6967/1480.full http://www.bmj.com/content/309/6967/1480.full https://en.wikipedia.org/wiki/Simpson's_paradox#Kidney_stone_treatme

http://www.bmj.com/content/309/6967/1480.full http://www.bmj.com/content/309/6967/1480.full https://en.wikipedia.org/wiki/Simpson's_paradox#Kidney_stone_treatment https://en.wikipedia.org/wiki/Simpson's_paradox#Kidney_stone... Has larger sample sizes, all would be valid for statistical analysis. Although this paradox isn't really dependent on a sample size, it is not so much a statistical test. It's a trend that can be observed when looking at sub groups compared to overall averages. In the above example open surgery was better for both small and large kidney stones and is a prime example of Simpson's paradox. However, one should note that Simpson's paradox can also be subject to further confounding variables. Perhaps patients that were suitable for open surgery were in better medical condition and therefore the open surgery sample was non-random and caused the higher success rate. The presence of Simpson's paradox, similar to correlation, also does not imply causation.