In statistics, the term ceiling effect refers to an effect whereby data cannot take on a value higher than some "ceiling." Ceiling effects present statistical problems similar to those of "floor effects". Specifically, the utility of a measurement strategy is compromised by a lack of variability. In the case of a ceiling effect, the majority of scores are at or near the maximum possible for the test. This presents two major problems.
First, the test is unable to measure phenomenon or traits above its ceiling. For example, a ceiling effect on an IQ test would be problematic because it suggests there are a substantial number of people with intelligence levels too high to be measured by the test. Thus, the test fails to distinguish between the people scoring at the top, or ceiling, of the test.
Second, most statistical procedures rely on scores being variable and evenly distributed. Often, statistical tests assume that scores are distributed in a "normal distribution", commonly called the bell curve. With strong ceiling effects, distributions are usually distorted with little variability. This violates statistical assumptions and limits the possibility of finding effects.