Excellent points, Doug! That's exactly why Uman reported the median in
his work. The median is a "resistant" statistic, but also nonlinear and
so has qualities that are difficult to model. The distributions of
observed peak lightning current looks to be sort of gamma, much like
rainfall amounts, though different data sets come up with slightly
different parameters.
Kim N5OP
On 8/9/2015 5:28 AM, kr2q@optimum.net wrote:
I was recently stated here that:
[snip]
With a number of samples large enough to be statistically valid a single,
significant outlier, be it
high or low can substantially skew the median, but have little effect on the
average.
[end snip]
Ummm. This is exactly backwards. The median is often used because it is
immune to outliers,
whereas the mean (average) is can be greatly impacted by the outliers.
EG: Grades in class. Lets say your grades are 95, 95, 95, 95, 95, 95, 95, 95,
95, 95.
What grade is typical? Well, 95! The mean (average) is 95 and the median is
95.
But what if you failed one quiz/test and got a 15?
Now your grades are 95, 95, 95, 15, 95, 95, 95, 95, 95, 95.
Your median grade is still 95, but your average is now 87.
So clearly, median was unaffected by an outlier, while the average moved a lot.
If you make "n" larger, you will still see the same thing; median is static
while average isn't.
For those who "love" using the mean (average), try using the geometric mean.
It greatly
lessens the impact of outliers. Of course, you have to account for that when
reporting out.
de Doug KR2Q
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--
Kim Elmore, Ph.D. (Adj. Assoc. Prof., OU School of Meteorology, CCM, PP
SEL/MEL/Glider, N5OP, 2nd Class Radiotelegraph, GROL)
/"In theory there is no difference between theory and practice. But, in
practice, there is." //– Attributed to many people; it’s so true that it
doesn’t matter who said it./
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