Various ways to show variability

Reader Doeke W. sends me to this chart.

I like many aspects of this exercise. This chart displays the results of an experiment conducted by a computer games company to show that the new build ("249") renders frames faster than the older build ("248"). The messages of the chart are clear: the 249 build (blue bars) is substantially faster, over 80% of the frames render in 7 miliseconds or fewer under 249 compared to less than 40% under 248, and less obviously, the variance of frame times is also significantly smaller.

The slight problem is that readers probably have to read the text to grasp most of the above.

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Using lines (or areas) improves the readability.

In the text, the author explains how to turn time per frame into frame per second, the more common way of measuring rendering speed. The formula is 1000 divided by time per frame. Wouldn't it be better if the chart plots fps directly?

When it comes to presenting distributions (or variability), the cumulative chart is more useful but it also is harder for readers to comprehend. For example:

The beauty of this chart is that one can take any point on the vertical axis, say, 80% level and read off the comparative values of 7 millisecond for the blue line (249) and 10.5 ms for the red (248). That means 80% of the 249 frames were rendered in fewer than 7 ms, relative to 10.5 ms for 248 frames.

Alternatively, taking a point on the horizontal axis, say 5 milliseconds, one can see that about 8% of 248 frames would reach that threshold but 30% of 249 frames did.

The steeper the ascent of the S-curve, the more efficient is the rendering.