Expected variance of subset of population

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The discussion centers on calculating the expected variance of a randomly selected subset Y of a population X, where Y contains n-1 elements. It is proposed that the expected variance of Y is less than the variance of X when Y is formed by randomly removing one element from X. The conversation suggests two approaches to proving this: a hard method involving the relationship between the sums of Y and X, and an easier method using the law of total variance. The law of total variance provides a more straightforward proof for this assertion. Overall, the expected variance of a subset formed by removing an element from a population is indeed less than that of the entire population.
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I want to calculate expected variance of a randomly selected subset of a population.

The particular problem I am trying to solve is as follows. There is a set of values X = {x1, ... , xn}. Let Y be subset of X with n-1 elements. I think that if Y is selected at random (that is, if is produced by randomly removing an element of X), the expected variance of Y is less than the variance of X. Is this right and if so is there a simply proof?
 
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A hard way: write sum(Y)=sum(X)-xj etc.

An easy way: The law of total variance.
 
The standard _A " operator" maps a Null Hypothesis Ho into a decision set { Do not reject:=1 and reject :=0}. In this sense ( HA)_A , makes no sense. Since H0, HA aren't exhaustive, can we find an alternative operator, _A' , so that ( H_A)_A' makes sense? Isn't Pearson Neyman related to this? Hope I'm making sense. Edit: I was motivated by a superficial similarity of the idea with double transposition of matrices M, with ## (M^{T})^{T}=M##, and just wanted to see if it made sense to talk...

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