:: A median can be mid-way between two values, but NOT a mode ( unless Stu was thinking of making a rough estimate of the mode of a continuous unimodal distribution from a sample of values ).
22.
It can be shown for a unimodal distribution that the median \ tilde { X } and the mean \ bar { X } lie within ( 3 / 5 ) 1 / 2 H " 0.7746 standard deviations of each other.
23.
It can be shown for a unimodal distribution that the median " ? " and the mean " ? " lie within ( 3 / 5 ) 1 / 2 H " 0.7746 standard deviations of each other.
24.
Maye, Weiss, and Aslin found that infants who were exposed to a bimodal distribution of a non-native contrast that was initially difficult to discriminate were better able to discriminate the contrast than infants exposed to a unimodal distribution of the same contrast.
25.
To see how this test works we assume or know " a priori " that the population from which the sample is drawn has a mean of " ? " 0 and that the population has a symmetrical unimodal distribution-a class that includes the normal distribution.
26.
For instance, McGeoch and Gaston ( 2002 ) show that the number of satellite ( rare ) species declines with the increase of sampling grains, but the number of core ( common ) species increases, showing a tendency from a bimodal OFD towards a right-skewed unimodal distribution.
27.
Because the mean and the mode in a unimodal distribution differ by at most " 3 standard deviations at most 5 % of a symmetrical unimodal distribution lies outside ( 2 " 10 + 3 " 3 ) / 3 standard deviations of the mean ( approximately 3.840 standard deviations ).
28.
Because the mean and the mode in a unimodal distribution differ by at most " 3 standard deviations at most 5 % of a symmetrical unimodal distribution lies outside ( 2 " 10 + 3 " 3 ) / 3 standard deviations of the mean ( approximately 3.840 standard deviations ).
29.
However it is often not known whether a particular process generates data that conform to particular distributions, fortunately the Chebyshev's inequality and the Vysochanskij Petunin inequality allow us to infer that for any unimodal distribution at least 95 % of the data will be encapsulated by limits placed at 3 sigma.
30.
While for typical unimodal distributions ( with centrally located modes, inflexion points at both sides of the mode, and longer tails ) ( with Beta ( " ? ", " ? " ) such that ) it is known that the sample mean ( as an estimate of location ) is not as realizations of a random walk is a much more robust estimator than the median ( which is an inappropriate sample measure estimate in this case ).
