That is true for small samples, but not those large enough to be
statistically significant.
The larger the sample, the more accurate statistically. With
lightening, 5 to 10% of the number of measured samples including
outliers would be appropriate. Even 5% would be a large number where
the outlier would have more of an effect on the median, than the
average. IE, one super strike (positive lightening) which could be 5
or more times the typical strike would raise the median substantially,
but have virtually no effect on the average. Population surveys with
margins of error of +/- 3% should contain thousands of samples.
At least that's what we were taught in CS and Math.
73
Roger (K8RI)
On 8/9/2015 6:28 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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73
Roger (K8RI)
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