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Descriptive Statistics for #WebPerf

Stats.  How geeky.  And boring.  I hardly remember anything from basic elementary math, let alone anything to do with statistical analysis at a college / university level.  But it turns out that statistics can be incredibly useful in describing what's happening across a population, such as when you use Real User Monitoring (RUM) to get performance metrics (such as load times) across your user base.

Why is this important?  Glad you asked.  There is well-accepted evidence from some of the largest e-tailers including Amazon and Wal-Mart on the effect of conversions and sales revenues with a site's performance.  Most of this data centers around median scores (in other words, the "average" performance of a page), where an increase (or decrease) in a page's median load times correlate to measurable changes in conversion rates.  The reason why we use median instead of mean is because median is much more resistant to outliers that may markedly affect the "average" number, and hence distort what we perceive as the typical experience when visiting a page.