And you are here why?

/via https://flowingdata.com/2017/05/02/summary-stat/ If you don’t pay attention to your’ data’s distribution, you run the risk of focusing on the wrong thing entirely. The perfect examples here are  the datasets generated by Justin Matejka & George Fitzmaurice  (•), where, thought wildly different, each dataset has the same summary statistics (mean, standard deviation, and Pearson’s correlation) to 2 decimal places! To really belabor this point, absent knowledge of the actual distribution, you  shouldn’t  rely on baseline summary statistics. Take  averages  for example: 1.  Outliers can — wildly! — skew your averages. You walk to work 200 days a year, and fly cross-country to the HQ in Los Angeles once a year. What’s the average distance to work? 2.  On the other hand, you averages can — totally — hide your outliers. Your DMV processes 290 out of 300 applications in less than 15 minutes, but the other ten take greater than 2 hours....

The good news around metrics is that it is increasingly easy to instrument and monitor your code, with plenty of usable software out there like  Prometheus ,  TICK ,  OpenTracing , and whatnot. The bad news is, well, pretty much the same — it’s so easy to instrument and monitor your code that the sheer amount of stuff that you can capture is mind-boggling. It is  so  easy to get caught up in all the charts and graphs that you will find yourself constantly losing sight of the the forest for the trees. All that data ticking around on the screen can, counter-intuitively, mean that it is actually  harder  to actually get any information (let alone knowledge!) from the metrics. Latency Latency is a particularly good example of this. Most everyone out there measures this as an  average , and while that’s better than nothing, it can easily hide issues. The fault, as always, lies in us — in that we tend to think about our systems in terms of steady-...