Talk:Standard deviation

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Field: Probability and statistics
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WikiProject Statistics (Rated C-class, Top-importance)

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Incorrect diagram under section "Interpretation and application" ?

The red and blue populations are supposed to have mean = 100 but from inspection the red one clearly appears to have a lower mean.

Please use proper colors in the drawings

The chosen colors are like a color vision test. Please use distinct and bright colors, especially because of green, yellow and red being too close. It's more important to be accessible than to pick colors by other factors. Bright yellow, bright green and red, make it more accessible. Thanks. I am referring to this picture in particular: Variance_visualisation.svg

Absolutely obtuse to a lay person

We have not done very well with our stated goal of making our articles accessible and understandable to the lay public with this article. After reading for around half an hour, following interminable links to jargon, and still being quite unclear as to exactly what Standard Deviation expresses ... I went to the "Math is Fun" site and within 5 minutes had a very clear understanding, including: what Standard Deviation means, how it is used, exactly how to calculate it, and even why squaring is involved in variance of which standard deviation is simply the square root. I often do minor edits to help Wikipedia and I donate money also, and I state this here only to show my strong belief in, and commitment to Wikipedia. So it is very disappointing to be forced off site to get simple clear answers. Thanks — Preceding unsigned comment added by 97.125.83.84 (talk) 18:35, 13 July 2016 (UTC)

Agreed. Pgpotvin (talk) 18:41, 21 July 2016 (UTC)

Weighted Standard Deviation

(1) There is mention of n′ at the end and n′ appears nowhere else in the section.

(2) The presentation of the same concept is much clearer in the article Mean square weighted deviation, which writes:

${\displaystyle s^{2}={\frac {\sum _{i=1}^{N}w_{i}}{{(\sum _{i=1}^{N}w_{i}})^{2}-{\sum _{i=1}^{N}w_{i}^{2}}}}\ .\ {\sum _{i=1}^{N}w_{i}(x_{i}-{\overline {x}}^{\,*})^{2}}}$

where ${\displaystyle {\overline {x}}^{\,*}}$ is the weighted mean (see that article for details). Again the corresponding standard deviation is the square root of the variance. This is much simpler to grasp and compute than the s, A, W or Q described in this section.