“Epidemiology is nothing if not a productive field. All that is needed for success is a database (larger the better), a disease (any will do), and some minor facility with statistical software.”
William Briggs writes:
Our latest example is the Environmental Health Perspectives1 paper “Residential Proximity to Freeways and Autism in the CHARGE Study” by Volk et al.
The authors found a group of mothers who lived in California. They measured the distance these mothers lived to “freeways and major roadways” for the majority of their pregnancies. They also took note whether their children developed autism. They posited that living closer to freeways increased the risk of autism. They also measured mothers’ education, age, and smoking status, the kids’ race and whether the kids were preemies.
They purposely identified 304 kids with autism and 259 without from a database “frequency matched by sex, age, and broad geographic area.” Ideally, since this data was hand-picked, they should have had equal numbers in each group, and equal frequencies of boys in each group. But the autism group had 87% boys, while the normal group had 81%. In other words, by design (purposeful or accidental), they put more boys in the autism group than they put in the control group. They gave this difference a “Chi-square p-value” of 0.10. What does that number mean? Well, nothing (see the footnote2)…