Randomized trials may not be definitive even when they show clear and indisputable evidence of both a statistically significant and practical difference between the average in the treatment group and the average in the control group. Statistically significant randomized trial results are not always useful to a doctor or nurse when there is substantial practical variation within the treatment group. That is why a statistically significant randomized trial is not definitive proof of causation. Think of a situation where the randomized trial ignores gender. What if a treatment works really well for men but actually does a little bit of harm to women with no benefit. The “average person” may do fairly well with the treatment and even show statistically significant improvement. But the “average person” does not exist. There are only men and women in the trial. The “average person” represents no one. The solution is to use control variables (such as gender) in a randomized trial in order to zero in on the subsets within the treatment group in order to sort out who will really benefit from the treatment and who will not benefit. This means using regression analysis (or analysis of covariance) on data from randomized trials. Hopefully, the new generation of doctors and nurses understand this and are not led astray by “highly significant” results from some randomized trials. This opens the door to the use of anecdotal evidence as useful in alerting a doctor or nurse that the results of a particular randomized trial may not be definitive for some types of patients.