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Comment by tobinfricke | Hacker News Reader

Omg yes. My coworkers often specify parameters (the results of what we would have called "regression" or "fitting" but what is now called "machine learning") to like 14 decimal places when we only know them to 20%. It would have yielded a failing grade in my high school chemistry class.

They are like "Chesterton's digits" when they appear unexplained in parameter files. Do we really know this number or is it just a wild-assed guess?

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> Do we really know this number or is it just a wild-assed guess?

Does it matter? You have 23 bits of mantissa to fill one way or the other, and if you don't know the lower, say, 12, one pattern of bits is as good as any other pattern.

Precision beyond what can be justified by the uncertainty of the original input is wrong, not just as-good-as.

In terms of actual numbers in the physical world, sure, but the usual kind of floating point numbers don't have a concept of precision, and you're getting all 23 bits whether you want them or not.

Humans know the difference between "1.0" and "1.0000" but they're the same to the computer.

rounding, especially in decimal, isn't actually any better. If you want to specify precision, specify it explicitly.

The reason you get rid of those extra digits is because it will always be tempting to find meaning in them.

Great answer!

That would be fine if the number were accompanied by a confidence interval.

puts on grumpy old man hat Yeah, back in my day you had to learn about significant digits because we still used slide rules and the calculators didn't have as many functions and we liked it! removes grumpy old man hat

That's not the reason sigfigs are important.

Oh, I'm aware. On the way to getting a bachelor's in Chemistry I was drilled on the importance of sigfigs as a way of preventing data contamination by artificial precision (e.g. trying to weigh 16.0000000000 lbs with a bathroom scale). It just seemed funnier this way...

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