Clinical trial outcome data can be presented and analyzed as mean change scores or as response rates. These two methods of presenting the data can lead to divergent conclusions. This article explores the reasons for the apparently divergent outcomes produced by these methods and considers their implications for the analysis and reportage of clinical trial data. It is shown that relatively small differences in improvement scores can produce relatively large differences in expected response rates. This is because differences in response rates do not indicate differences in the number of people who have improved; they indicate differences in the number of people whose degree of improvement has pushed them over a specified criterion. Therefore, patients classified as non-responders may have shown substantial and clinically significant improvement, and these are the patients who are most likely to become responders when given medication. Response rates based on continuous data do not add information, and they can create an illusion of clinical effectiveness.