| > You're talking like sig figs is error propagation, but it isn't. No, that is the exact opposite of what I said! For starters, "uncertainty" and "error" are not the same thing here. I am saying significant figures in a measurement encapsulates an inherent measurement-specific uncertainty conveyed by significant figures, and that this uncertainty must be considered when doing calculations with that measurement. Just like the person I responded to, I don't think you've thought about why significant figures actually exist in the first place. > The correct solution is error propagation (with appropriate estimates of the errors of the inputs), not arbitrarily rounding numbers at each step Nowhere in my comment did I arbitrarily round anything. I thoughtfully propagated the uncertainty, which is why it was +/- 0.01m in the measurement and +/- 0.02m^2 in the calculation. The whole point of my argument is that uncertainties in calculated quantities can be rigorously determined from the uncertainty of the inputs, and measurement inputs have uncertainty determined by the significant figures. On the other hand, ignoring significant figures in calculations means we're ignoring a potential source of uncertainty in downstream analysis. If you think significant figures is about "arbitrarily rounding something" then you are thoughtlessly applying high school chemistry rules. Please read this carefully: If I measure something with a meterstick that is broken down into centimeters, that measurement has an inherent uncertainty of either +/- 1cm or +/- 0.5cm - which one you use is a problem-specific choice similar to p95 or p99 for statistical significance (if it was a physical meterstick I'd choose 1cm because human eyeballs aren't very good; if it was laser inference I'd choose 0.5cm). So if I am a data scientist with a database of direct measurements from a meterstick, each one has an inherent uncertainty of +/- 0.01m that's implied by the data source even if it's not in the database. This is the entire point of representing the data as 12.03m, 1.00m, etc, instead of 1.234m. If you represented a measurement as 1.234m that would imply your meterstick could measure decimeters, but it probably can't. So 1.234m isn't merely against the rules, it's inaccurate. If you take a measured side length of 1.00m and say the calculated area is 1.00m^2, then naively someone might think the uncertainty in the area is +/- 0.01m^2 based on thoughtlessly applying dumb high school chemistry rules. But that's not true, the uncertainty in the calculated area is in reality +/- 0.02m^2. The measurement can be presented without an explicit +/- because the significant figures acts as a "shorthand" and we don't need to do calculations to estimate the uncertainty. But the calculation must present a calculated uncertainty. Programmers and data scientists are lazy about significant figures because they don't care where the data is coming from, to them it's all doubles in a database, and significant figures is just a matter of rounding things correctly at the end. The area-of-a-square argument proves that this is a mistake. |
You are explaining error propagation, but my point is that _if you are doing error propagation (as you should do if you want to do things properly), significant figures ARE just for making deliverable pretty_.
You are talking about measurement uncertainty. Measurement uncertainty is written x +- y, with y being the uncertainty.
If you don't do that and use significant digit instead, you lose the information and precision: 10.0 +- 0.1 is 10.0, 10.0 +- 0.2 is 10.0, 10.0 +- 0.3 is 10.0, ...
This is why the other person was talking about "arbitrarily rounding".
You should _never_ said "well, it's a measurement of 10.0 with a 0.2 precision, so I can write 10.0", you should _always_ write 10.0 +- 0.2 (in which case, you can also write 10 +- 0.2 or 10.000 +- 0.2, the significant digits have no impact on any future results). Writing 10.0 instead of 10.0 +- 0.2 is just a terrible practice that does not have much justification, 10 +- 0.2 is always a better way. (and my point is that the problem you have with the significant number disappear if you teach people to use a non-clumsy way)
(and, no, you should not do the distinction "it's a measurement, so it's written differently", because in practice, a lot of "measurements" are in fact already a transformation, and sometimes you cannot even know for sure yourself. For example, a temperature sensor will measure an electrical resistance (with a measurement uncertainty) and then convert it into a temperature, and according to you, it should not be written the same way, just for arbitrary reasons)