Standard Deviation of a sample of a population's means

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Discussion Overview

The discussion revolves around the derivation of the standard deviation of the means of samples from a population, specifically addressing whether the standard deviation formula arises from theoretical principles or experimental results. The scope includes theoretical reasoning and mathematical derivation.

Discussion Character

  • Technical explanation
  • Mathematical reasoning
  • Debate/contested

Main Points Raised

  • Oscar questions the derivation of the standard deviation of sample means, asking if it is a result of experimentation or theoretical derivation.
  • Some participants assert that the result is derived, particularly under the assumption that the population is normally distributed.
  • One participant provides a mathematical derivation of the variance of the sample mean, indicating that if the variables are independent and identically distributed, the variance of the sample mean is \(\frac{\sigma^2}{n}\), leading to the standard deviation being \(\frac{\sigma}{\sqrt{n}}\).
  • There is a correction regarding the notation in the variance calculation, with participants discussing the importance of the square in the numerator and the root in the denominator.
  • Participants acknowledge mistakes in their calculations, with one expressing regret over a misunderstanding related to the derivation process.

Areas of Agreement / Disagreement

Participants generally agree on the mathematical derivation of the standard deviation of sample means, but there are minor disagreements regarding notation and clarity in the derivation process. The discussion does not reach a consensus on whether the derivation is purely theoretical or influenced by experimental results.

Contextual Notes

Some assumptions about the distribution of the population and the independence of samples are implied but not explicitly stated. The discussion also reflects some confusion regarding the notation used in variance and standard deviation calculations.

Who May Find This Useful

This discussion may be useful for students or individuals interested in understanding the statistical properties of sample means, particularly in the context of normal distributions and variance calculations.

2^Oscar
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Hey guys,

Just a question which has been puzzling me for some time.

I am told that means of samples of a population can be normally distributed with mean of [tex]\mu[/tex] and with standard deviation of [tex]\sigma[/tex]/[tex]\sqrt{}[/tex]n

Can someone please explain to me how the standard deviation is derived or is it ismply as a result of experimentation?

Thanks,
Oscar
 
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It is derived. The given result follows if the population is normal.
 
The mean is

[tex] \overline X = \frac{\sum X}{n}[/tex]

If all of the [tex]X[/tex] variables are independent, and identically distributed, then

[tex] Var[X] = Var\left(\frac{\sum X}n\right) = \frac 1 {n^2} \sum Var(X) = \frac{n\sigma^2}{n^2} = \frac{\sigma} n[/tex]

so the standard deviation is as you state.

If the [tex]X[/tex] values are themselves normally distributed, then the mean is as well (linear combinations of normally distributed normal random variables are normal)

If the [tex]X[/tex] values are not normally distributed, the distribution of [tex]\overline X[/tex] is approximately normal if certain conditions are satisfied.
 
statdad said:
[tex] Var[X] = Var\left(\frac{\sum X}n\right) = \frac 1 {n^2} \sum Var(X) = \frac{n\sigma^2}{n^2} = \frac{\sigma} n[/tex]

You forgot the root in the denominator.

CS
 
stewartcs said:
You forgot the root in the denominator.

CS
No, actually I forgot the square in the numerator, since I was writing out the variance. :frown:
Still a stupid mistake on my part.

The variance is

[tex] \frac{\sigma^2} n[/tex]

so the standard deviation is

[tex] \frac{\sigma}{\sqrt n}[/tex]
 
statdad said:
No, actually I forgot the square in the numerator, since I was writing out the variance. :frown:
Still a stupid mistake on my part.

The variance is

[tex] \frac{\sigma^2} n[/tex]

so the standard deviation is

[tex] \frac{\sigma}{\sqrt n}[/tex]

Sorry...it appeared as if you were answering the OP's question about the standard deviation directly which is why I assumed you had finished the derivation all the way out to the standard deviation and not just the variance.

CS
 
stewartcs; there is no need for you to apologize and I certainly did not mean to imply (in my earlier post) that there was. I am sorry if it seemed that way.
 
Hey guys,

Thanks very much for showing me the derivation - it has helped make things a lot clearer :)

Thanks again,

Oscar
 

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