3 ms·

Maybe I'm missing something about the voltmeter example. My assumption is that the 100-volt-maximum meter can distinguish between 100 volts and more than 100 vo

Maybe I'm missing something about the voltmeter example. My assumption is that the 100-volt-maximum meter can distinguish between 100 volts and more than 100 volts, in which case there's no problem. If the voltmeter doesn't accurately indicate whether or not a measurement is outside of its range then the statistician is correct that everything should be re-measured. Do some people think that the possibility of not being able to take accurate measurements is the same as not having taken accurate measurements? EDIT: Maybe the ambiguity is in what the engineer would have recorded if finding a voltage >100 volts while the other meter was broken? It's like undefined behavior in programming; if you know your software will have undefined behavior when encountering certain data then you can't trust whether the output is valid unless there's independent confirmation that the data won't cause undefined behavior. If the statistician doesn't have certainty that the engineer will have defined behavior (e.g. say "I couldn't complete the measurements" vs. undefined behavior like writing down "99" or exploding) then they of course want to re-measure.

tinalumfoil 5y ago
> in which case there's no problem I have a box with 1000 tubes, exactly 1% of which are beyond my capacity to measure. I take a random sample of 20, and call that a roughly 80% chance of being able too measure all of them. Let's say the average is 50V and the ones beyond my measurement average 150V. We can use this info to solve for the average of the tubes we can measure: 25V. So, in 80% of the cases we'll get an average measurement of 25V. In 20% of the cases we're going to realize our mistake, buy a better voltmeter, and redo everything. In this case we can measure everything so we get the correct number: 50V. 25V * 0.8 + 50 * 0.2 = 30V This, of course, won't be statistically significant. But let's get a lot of researchers together to measure a lot of tubes. And let's say 100V limit meters are common so half make the same mistake. Now we have a statistically significant 40V. Wrong, wrong wrong.

tinalumfoil 5y ago

> in which case there's no problem I have a box with 1000 tubes, exactly 1% of which are beyond my capacity to measure. I take a random sample of 20, and call that a roughly 80% chance of being able too measure all of them. Let's say the average is 50V and the ones beyond my measurement average 150V. We can use this info to solve for the average of the tubes we can measure: 25V. So, in 80% of the cases we'll get an average measurement of 25V. In 20% of the cases we're going to realize our mistake, buy a better voltmeter, and redo everything. In this case we can measure everything so we get the correct number: 50V. 25V * 0.8 + 50 * 0.2 = 30V This, of course, won't be statistically significant. But let's get a lot of researchers together to measure a lot of tubes. And let's say 100V limit meters are common so half make the same mistake. Now we have a statistically significant 40V. Wrong, wrong wrong.