I have mixed feelings. On the one hand, yes, people working with data should be more thoughtful about how many digits they report. But I’m not a big fan of the

I have mixed feelings. On the one hand, yes, people working with data should be more thoughtful about how many digits they report. But I’m not a big fan of the proposed solution (report everything to a small fixed number of significant digits, like two). Massive computer experiments are common and (often) cheap. If I’m comparing two classification models on a large dataset a difference between 97% and 96.8% accuracy might well be both statistically and practically significant. An error I’ve seen professional statisticians make is asserting that averaging samples (or performing more complex statistical analyses like regression) justifies increasing the number of significant digits. For example, if I have a hundred observations measured with two significant digits of precision, the mean of those observations is asserted to be accurate to three significant digits, the standard deviation of the mean being 1/10th that of each observation. This is a natural conclusion if you treat the significant digits as accounting only for independent random errors, but in fact significant digits are also used to account for systematic errors in measuring equipment. A scale that measures down to 0.1g, for example, may be miscalibrated by 0.03g. This makes reporting the mean of 100 samples down to 0.01g obviously nonsensical. In short, how many digits to report deserves more thought than either most data scientists or the author give it.