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copied!<p>You can sample randomly from your set ("<a href="http://en.wikipedia.org/wiki/Statistical_population" rel="noreferrer">population</a>") to get an average ("<a href="http://en.wikipedia.org/wiki/Arithmetic_mean" rel="noreferrer">mean</a>"). The accuracy will be determined by how much your samples vary (as determined by "<a href="http://en.wikipedia.org/wiki/Standard_deviation#Estimating_population_SD" rel="noreferrer">standard deviation</a>" or variance). </p> <p>The advantage is that you have billions of observations, and you only have to sample a fraction of them to get a decent accuracy or the "<a href="http://en.wikipedia.org/wiki/Confidence_interval" rel="noreferrer">confidence range</a>" of your choice. If the conditions are right, this cuts down the amount of work you will be doing.</p> <p>Here's a <a href="http://www.extremeoptimization.com/" rel="noreferrer">numerical library</a> for C# that includes a random sequence generator. Just make a random sequence of numbers that reference indices in your array of elements (from 1 to <em>x</em>, the number of elements in your array). Dereference to get the values, and then calculate your mean and standard deviation.</p> <p>If you want to test the distribution of your data, consider using the <a href="http://www.itl.nist.gov/div898/handbook/eda/section3/eda35f.htm" rel="noreferrer">Chi-Squared Fit</a> test or the <a href="http://www.itl.nist.gov/div898/handbook/eda/section3/eda35g.htm" rel="noreferrer">K-S</a> test, which you'll find in many spreadsheet and statistical packages (e.g., <a href="http://www.r-project.org/" rel="noreferrer">R</a>). That will help confirm whether this approach is usable or not.</p>

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