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    When they say standard deviation do they mean using n or n-1.

    I know use n is sd and n-1 is sample deviation but MEI don't seem to know this some of the time but still use it some of the time. Sometimes they call it rmsd sometimes they say they are different.

    They seem to be completely inconsistent and its starting to annoy me :mad:
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    I could be wrong, but I have a suspicion that what you are referring to is the different cases of a sample and a population. I have forgotten the relevant formulae, and which of n and n-1 refers to which case, but if you have a sample then you make the standard deviation greater than when you were doing the same calculation with a population. The reasoning behind this is obvious.
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    (Original post by Dadeyemi)
    When they say standard deviation do they mean using n or n-1.

    I know use n is sd and n-1 is sample deviation but MEI don't seem to know this some of the time but still use it some of the time. Sometimes they call it rmsd sometimes they say they are different.

    They seem to be completely inconsistent and its starting to annoy me :mad:
    n-1 is used for samples and is called the standard deviation in MEI.
    n is used for samples only when we refer to the root mean square deviation (rmsd) in MEI.


    n-1 is used because of the degrees of freedom in the sample. When we have a constant mean, after collecting n-1 data, the nth value is not 'allowed' to vary any more. So, say, if we have 5 samples and the deviations of the first four are '-1', '-2', '3', '1', the last one's deviation will inevitably be '-1', as the sum of deviations will always have to equal zero (so that '-1-2+3+1-1 = 0').

    You could prove that this the standard deviation formula produces an unbiased estimator of the square root of variance, if you do S4 :p:
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    (Original post by Aurel-Aqua)
    n-1 is used for samples and is called the standard deviation in MEI.
    n is used for samples only when we refer to the root mean square deviation (rmsd) in MEI.


    n-1 is used because of the degrees of freedom in the sample. When we have a constant mean, after collecting n-1 data, the nth value is not 'allowed' to vary any more. So, say, if we have 5 samples and the deviations of the first four are '-1', '-2', '3', '1', the last one's deviation will inevitably be '-1', as the sum of deviations will always have to equal zero (so that '-1-2+3+1-1 = 0').

    You could prove that this the standard deviation formula produces an unbiased estimator of the square root of variance, if you do S4 :p:
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    I was stalking your profile, :sorry:
    Ah thanks It's just anoying that MEI chose to disagree with the rest of the word and themselves as the seem to use it as n in S2.

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    (Original post by Dadeyemi)
    Ah thanks It's just anoying that MEI chose to disagree with the rest of the word and themselves as the seem to use it as n in S2.

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    Stalk all you want I have nothing to hide :ninja:
    They use n is S2? Give me an example (I have the book next to me). Are you referring to the Central Limit Theorem?
 
 
 
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