<P> Any probability distribution on R has at least one median, but there may be more than one median . Where exactly one median exists, statisticians speak of "the median" correctly; even when the median is not unique, some statisticians speak of "the median" informally . </P> <P> The medians of certain types of distributions can be easily calculated from their parameters; furthermore, they exist even for some distributions lacking a well - defined mean, such as the Cauchy distribution: </P> <Ul> <Li> The median of a symmetric unimodal distribution coincides with the mode . </Li> <Li> The median of a symmetric distribution which possesses a mean μ also takes the value μ . <Ul> <Li> The median of a normal distribution with mean μ and variance σ is μ . In fact, for a normal distribution, mean = median = mode . </Li> <Li> The median of a uniform distribution in the interval (a, b) is (a + b) / 2, which is also the mean . </Li> </Ul> </Li> <Li> The median of a Cauchy distribution with location parameter x and scale parameter y is x, the location parameter . </Li> <Li> The median of a power law distribution x, with exponent a> 1 is 2 x, where x is the minimum value for which the power law holds </Li> <Li> The median of an exponential distribution with rate parameter λ is the natural logarithm of 2 divided by the rate parameter: λ ln 2 . </Li> <Li> The median of a Weibull distribution with shape parameter k and scale parameter λ is λ (ln 2). </Li> </Ul> <Li> The median of a symmetric unimodal distribution coincides with the mode . </Li>

When do we need to compute for the median