When profiling slow queries/code I often collect the elapsed time of a test where I take 5-10 runs and calculate the mean/average, standard deiviation, min, and

When profiling slow queries/code I often collect the elapsed time of a test where I take 5-10 runs and calculate the mean/average, standard deiviation, min, and max. As well as using line charts on the average, I've used a box plot (with the edges of the box being the mean +/- 1 standard deviation) to get an idea of whether a given change is significant or not. I.e. if the boxes are close together I will ignore a change I've made, only committing changes that provide a significant jump in performance. The box plot is a useful way of visualizing that. They can help with seeing highly variable performance (long box) from consistent performance (narrow box). I can see this in the data (mean, standard deviation) but having it represented visually can help -- especially looking at the data over several iterations, or when looking for patterns from changing a variable (like the number of items in the data being processed). I've also used linear regression calculations when data has looked linear or quadratic to check/confirm that assumption. -- You can overlay that on top of the data by computing the values for each value of n along side the actual data average and then including the average and calculated values in a line chart.