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  <h3>Sample Mean (<IMG SRC="../pics/stats/Xbar20w_25h.gif" WIDTH="20" HEIGHT="25">)</h3>
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The statistic <IMG SRC="../pics/stats/Xbar20w_25h.gif" WIDTH="20" HEIGHT="25"> is a measure on a sample that is used an an estimate of the mean (<IMG ALIGN=ABSBOTTOM HSPACE=0 SRC="/pics/stats/mu_x_24pt_clr_bkgrnd.gif" ALT="Mu x" WIDTH="22" HEIGHT="21">) of the population from which it was drawn. Regardless of the size of the population and regardless of the size of the random sample, it can be shown (through The Central Limit Theorem) that if we repeatedly took random samples of the same size from the same population, the sample means would cluster around the exact value of the population mean.  Another way to say this is to assert that <IMG SRC="../pics/stats/Xbar20w_25h.gif" WIDTH="20" HEIGHT="25"> is an unbiased estimate of <IMG ALIGN=ABSBOTTOM HSPACE=0 SRC="/pics/stats/mu_x_24pt_clr_bkgrnd.gif" ALT="Mu x" WIDTH="22" HEIGHT="21">.
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As illustrated here, our random sample contains 4 items and it was drawn from a population that contains 9 items. Most statisticians use (n) to represent the number of items in a sample, whereas they use the symbol (N) to represent the number of items in a population. For this sample from this population n=4, N=9.
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<IMG ALIGN=ABSBOTTOM HSPACE=10 SRC="/pics/stats/sgsmplme.gif" ALT="Image for Sample Mean" WIDTH="502" HEIGHT="238">
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