It depends on what you consider “accurate”. For home/diy usage, +/-10% may be absolutely sufficient as long as the error is stable (i.E: it always is a similar offset). For lab/industrial usage, 0.1% may be too far off. There’s a large gap in engineering effort that drives up the prices for more precise sensors.
I may have used ‘exactly’ sloppy here. What I mean is: When the context doesn’t say otherwise, one should assume accuracy or precision as rounding to the least significant digit given. I.e. by default 1kg may indicate a mass between 500g and 1.499g. If the accuracy is about 10g you should write 1.00kg.
We're not trying to hit a comet with a rocket here. 1 significant figure is more than sufficient for an initial consultation. Any additional accuracy required would be billable follow-on work.
If you expect bit-for-bit reproducible results, then yea, you'd have to know about the nitty-gritty details. The values should usually still correspond to the same thing in common real world precision though.
Agreed. 20% margin of error isn't really that relevant to me either.
I don't really care that much if I'm 70mg/dl or 100mg/dl if the line is horizontal. Both values are, roughly, in the "OK Zone" that I just don't care about better precision.
Even if I'm going low, the difference between 50 or 70 isn't nearly as relevant as the rate of change. If I'm going down, but the slope is gentle enough, I know I probably don't need much to get back to normal, while if it's steep going down, I probably need to eat something regardless if I'm 50 or 70, or 80.
As long as your measurement is a random sample everything is okay. Even if it is not, it is much more information than you had before. You just need to keep it in mind when evaluating conclusions. We are not talking about drug tails here and no one dies if the measurement is not 100% accurate.
I am able to measure everything 100% accurate. But this is really really expensive. It's a trade-of.
It always surprised me to see them reporting results to a 10th of a percent precision (e.g. "2.3%"). I'd assumed that the best they could do on an individual was a couple of orders of magnitude less precise (e.g. 10%, 20%)
Being precisely inaccurate isn't optimal. There needs to be a balance between precision and accuracy for a measure that's close to reality. Of course, there's going to be tradeoffs between the two, as well.
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