Unlearning descriptive statistics
I think the message of the article is great: move beyond the &quot;standard&quot; descriptions and pay more attention to what you&#x27;re trying to show and who your audience is.<p>That said, it&#x27;s a slight pet peeve of mine when people recommend the median over the mean to describe center. The median, on its own, does not describe what is &quot;typical&quot; any more than the mean does; it just has a small advantage in that it will always map a real observation, so for discrete data you don&#x27;t end up with things like &quot;1.9 legs.&quot; (That said, a mean of 1.9 legs actually seems much more informative to me than a median of 2 legs, so even in that case I prefer the mean.) It&#x27;s easy to envision many situations where the median&#x27;s representation is wildly inaccurate, just as you can imagine ways in which the mean can be misleading.<p>The median is also insensitive to skew, which is often cited as a good thing, but really that&#x27;s something you should determine on a case-by-case basis. In many (most?) applications, there&#x27;s no tangible benefit in having a measure of center that ignores skew. The median&#x27;s insensitivity also creates strange situations where subgroups in the population end up unrepresented (e.g. if the poorest 20% become even poorer because of changes to the tax code, the median doesn&#x27;t budge).  In general, the median conveys remarkably little information. It&#x27;s great at showing the center, but it gives zero indication of anything else. The mean certainly has its issues, but it&#x27;s far from inferior. Use the median when the situation calls for it, certainly, but realize that it&#x27;s just as limited as any other measure, if not more so.