Timeline for Is the use of standard deviation built on the assumption of normal distribution?
Current License: CC BY-SA 3.0
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| Mar 23, 2017 at 23:17 | comment | added | kjetil b halvorsen♦ | +1. But note that while variance (together with the mean) gives a complete description in the normal case, for nonnormal distribution this might no longer be the case, and other d3scriptors of the data might be much better | |
| Mar 23, 2017 at 22:50 | history | edited | Matthew Gunn | CC BY-SA 3.0 |
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| Mar 23, 2017 at 15:06 | history | edited | Matthew Gunn | CC BY-SA 3.0 |
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| Mar 23, 2017 at 14:47 | comment | added | Matthew Gunn | @whuber Yeah, I had started writing a CLT example (and now I've added it). The CLT is an extremely practical reason to care about the variance. | |
| Mar 23, 2017 at 14:42 | history | undeleted | Matthew Gunn | ||
| Mar 23, 2017 at 14:42 | history | edited | Matthew Gunn | CC BY-SA 3.0 |
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| Mar 23, 2017 at 14:36 | history | edited | Matthew Gunn | CC BY-SA 3.0 |
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| Mar 23, 2017 at 14:31 | history | deleted | Matthew Gunn | via Vote | |
| Mar 23, 2017 at 14:27 | history | edited | Matthew Gunn | CC BY-SA 3.0 |
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| Mar 23, 2017 at 14:02 | comment | added | whuber♦ | Chebyshev's Inequality is not specific to the variance: an equally useful version exists for every absolute moment with power greater than $1$. I would therefore suggest looking elsewhere for reasons why the SD is important and (almost) universal, such as the unique role played by variance in the Central Limit Theorem. | |
| Mar 23, 2017 at 13:15 | history | edited | Matthew Gunn | CC BY-SA 3.0 |
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| Mar 23, 2017 at 13:10 | history | answered | Matthew Gunn | CC BY-SA 3.0 |