3 ms·
No estimate of uncertainty in his measurements so he can't really tell who's most right.
by foxglacier 7mo ago
No estimate of uncertainty in his measurements so he can't really tell who's most right.
- kqr 7mo agoWouldn't the successive measurements contain some information of that uncertainty, if we assume the cooling rate is relatively smooth, locally in time? A logarithmic fit to their data indicate a standard deviation of 1 ℃ in the residuals. This includes both model error (the logarithmic fit is not that tight) and errors in my transcription from the plot, so the actual uncertainty of the measurements is probably even less. (The logarithmic fit was lazy. I tried a dual exponential fit and the standard deviation of residuals dropped to 0.45 ℃. Appears that measurement error is very small.)
- foxglacier 7mo agoThere could be a consistent bias due to the placement of the thermometer. You can't expect the LLM to assume that the temperature of the water means the temperature in a bottom corner of the cup which I guess is where the thermometer's sensor was. If he had told it how he would place the thermometer, then it could have known that, otherwise, what if it's being clever and finding an average temperature or one that would be measured at some other location? This seems qualitatively consistent with the fact that most models predicted higher temperature than what he measured because of hot water rising and cooling being greatest near the walls of the cup. This isn't really uncertainty so much as not defining the meaning of "temperature of the water".
- rcxdude 7mo agoThis is one of those 'you can just look at it' sorts of datasets, it's really not plausible that the uncertainty in the measurement is affecting anything.