Ceiling effect

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In pharmacology, the term ceiling effect refers to the property of increasing doses of a given medication to have progressively smaller incremental effect (an example of diminishing returns). Narcotics, such as nalbuphine, serve as a classic example of the ceiling effect; increasing the dose of a narcotic frequently leads to smaller and smaller gains in relief of pain. In many cases, the severity of side effects from a medication increases as the dose increases, long after its therapeutic ceiling has been reached.

In statistics, the term ceiling effect refers an effect whereby data cannot take on a value higher than some "ceiling." Ceiling effects present statistical problems similar to those of "floor effect". 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.

An alternative less intuitive example of a ceiling effect can be found in cognitive neuroscience testing of response time to a particular stimulus. For example, rather than the ceiling being a high number, which would indicate a longer response time (e.g. in milliseconds), the ceiling is the shortest possible response time. For this to occur it might be assumed that all variables that could prevent the most rapid response had been controlled, thus allowing an individual to respond at their quickest. Moreover, when a ceiling occurs in response time data (e.g. a very rapid response time – in the vicinity of 250ms for a manual key press in some people), it still has the classic reduced variance of a ceiling effect, although in this circumstance it is not necessarily an unwanted effect. Rather, it simply indicates that response time has reached some kind of limit point.