Statistical Assumption
Statistical assumptions are general assumptions about statistical populations.
Statistics, like all mathematical disciplines, does not generate valid conclusions from nothing. In order to generate interesting conclusions about real statistical populations, it is usually required to make some background assumptions. These must be made with care, because inappropriate assumptions can generate wildly inaccurate conclusions.
The most commonly applied statistical assumptions are:
- independence of observations from each other: This assumption is a common error. (see statistical independence)
- independence of observational error from potential confounding effects
- exact or approximate normality of observations: The assumption of normality is often erroneous, because many populations are not normal. However, it is standard practice to assume that the sample mean from a random sample is normal, because of the central-limit theorem. (see normal distribution)
- linearity of graded responses to quantitative stimuli (see linear regression)
Read more about Statistical Assumption: Types of Assumptions, Checking Assumptions
Famous quotes containing the word assumption:
“The methodological advice to interpret in a way that optimizes agreement should not be conceived as resting on a charitable assumption about human intelligence that might turn out to be false. If we cannot find a way to interpret the utterances and other behaviour of a creature as revealing a set of beliefs largely consistent and true by our standards, we have no reason to count that creature as rational, as having beliefs, or as saying anything.”
—Donald Davidson (b. 1917)