In my classes I regularly tell students that statistical controversy is rarely about the number crunching, it's about the data.
I recently posted a link to an article about political polling. Summary? The data was wrong so the predictions were wrong.
However, there's another data problem and, perhaps, it's the most common problem. What should you do when you think something is true but you have data that contradicts it? Statistically, you're supposed to go with the data and change what you thought was true.
People who know statistics might recognize this situation. You have a null hypothesis, Ho, and the data leads to a small p-value. This means you should reject Ho and conclude that the alternative hypothesis, Ha, is true instead.
Unfortunately people tend to fall the other way. If the data contradicts their beliefs, they reject the data. This is true even among those who have scientific and statistical training.
The People's Pharmacy recently wrote about a new study that "exonerates" eggs. This is one of many studies that contradict that old idea the eggs are unhealthy because of dietary cholesterol. All the data shows that eggs are healthy. Yet the article states:
"It’s hard to teach old dogs new tricks. Many health professionals will find it challenging to accept the new data from the Finnish Heart Study. But the writing has been on the wall for quite a few years that the evidence supporting dietary cholesterol as the culprit behind heart disease was weak."
Health professionals, people with significant scientific training, will stick with their old beliefs and reject the data.