Editing Simpson's paradox (section)

== Criticism ==
One criticism is that the paradox is not really a paradox at all, but rather a failure to properly account for confounding variables or to consider causal relationships between variables.<ref>{{Cite journal |last=Blyth |first=Colin R. |date=June 1972 |title=On Simpson's Paradox and the Sure-Thing Principle |url=http://www.tandfonline.com/doi/abs/10.1080/01621459.1972.10482387 |journal=Journal of the American Statistical Association |language=en |volume=67 |issue=338 |pages=364–366 |doi=10.1080/01621459.1972.10482387 |issn=0162-1459}}</ref> Focus on the paradox may distract from these more important statistical issues.<ref>{{Cite journal |last1=Hernán |first1=Miguel A. |last2=Clayton |first2=David |last3=Keiding |first3=Niels |date=June 2011 |title=The Simpson's paradox unraveled |journal=International Journal of Epidemiology |volume=40 |issue=3 |pages=780–785 |doi=10.1093/ije/dyr041 |issn=1464-3685 |pmc=3147074 |pmid=21454324}}</ref>

Another criticism of the apparent Simpson's paradox is that it may be a result of the specific way that data are stratified or grouped. The phenomenon may disappear or even reverse if the data is stratified differently or if different confounding variables are considered. Simpson's example actually highlighted a phenomenon called noncollapsibility,<ref>{{Cite journal |last=Greenland |first=Sander |date=2021-11-01 |title=Noncollapsibility, confounding, and sparse-data bias. Part 2: What should researchers make of persistent controversies about the odds ratio? |url=https://www.jclinepi.com/article/S0895-4356(21)00182-7/fulltext |journal=Journal of Clinical Epidemiology |language=English |volume=139 |pages=264–268 |doi=10.1016/j.jclinepi.2021.06.004 |issn=0895-4356 |pmid=34119647|doi-access=free }}</ref> which occurs when subgroups with high proportions do not make simple averages when combined. This suggests that the paradox may not be a universal phenomenon, but rather a specific instance of a more general statistical issue.

Despite these criticisms, the apparent Simpson's paradox remains a popular and intriguing topic in statistics and data analysis. It continues to be studied and debated by researchers and practitioners in a wide range of fields, and it serves as a valuable reminder of the importance of careful statistical analysis and the potential pitfalls of simplistic interpretations of data.