Drexel dragonThe Math ForumDonate to the Math Forum

Ask Dr. Math - Questions and Answers from our Archives
_____________________________________________
Associated Topics || Dr. Math Home || Search Dr. Math
_____________________________________________

Difference in Standard Deviation Formulas

Date: 01/31/2006 at 17:01:11
From: Stefan
Subject: Standard Deviation

In regards to sample vs. population standard deviation, why is (n-1)
used in the denominator rather than n?



Date: 02/01/2006 at 00:52:14
From: Doctor Wilko
Subject: Re: Standard Deviation

Hi Stefan,

Thanks for writing to Dr. Math!

The answer would be found in any graduate-level book on probability. 
You would have to look up topics on biased/unbiased estimators.

The first question for us to answer is "What's an estimator?".

Definition:  An estimator is a rule/formula that tells us how to 
calculate the value of an estimate based on the measurements 
contained in the sample.  

Let's think about this using means (averages).

For example, if we take a random sample of 20 test scores from a 
class of 35 students, we can try to estimate the entire class 
average (population mean) by calculating the sample mean from these 
20 scores.  This sample mean is an estimator of the true population 
mean.  The rule/formula in this example is to take the 20 scores, 
add them up, and divide by 20.  The answer we get, the sample mean, 
is an estimator--our best inference about the true population mean 
(which we don't know, that's why we're trying to estimate it).

Now that we know what an estimator is, the short answer to your 
question is that sample variance (an estimator) COULD'VE been 
defined as "the sum of the squares of each value minus the sample 
mean divided by n", but it turns out that this is a BIASED estimator.

So, what's a biased estimator?  

A biased estimator in this case means that the sample variance 
calculated this way (divided by n) underestimates the true 
population variance.  If we are trying to make an inference about the 
true population variance, we don't want to underestimate, we want to be 
as accurate as we can!

So, using some theory from probability, it can be shown that this 
biased sample variance (as defined by dividing by n) can be made 
UNBIASED by dividing by (n-1) instead.

So, that's the reason we divide by (n-1)--to get an unbiased 
estimator.  Now we have a good estimator that we can use when making 
inferences about a population!

Does this help?  Please write back if you have further questions.


- Doctor Wilko, The Math Forum
  http://mathforum.org/dr.math/ 
Associated Topics:
College Statistics
High School Statistics

Search the Dr. Math Library:


Find items containing (put spaces between keywords):
 
Click only once for faster results:

[ Choose "whole words" when searching for a word like age.]

all keywords, in any order at least one, that exact phrase
parts of words whole words

Submit your own question to Dr. Math

[Privacy Policy] [Terms of Use]

_____________________________________
Math Forum Home || Math Library || Quick Reference || Math Forum Search
_____________________________________

Ask Dr. MathTM
© 1994-2013 The Math Forum
http://mathforum.org/dr.math/