Your measurements are more precise, which is actually a good thing. Rounding them to match the less-precise public data might feel like you’re making things consistent, but what you’re really doing is tossing out useful info. And when you’re running a hypothesis test, that extra decimal can matter—especially if the differences you’re trying to detect are subtle.
Here’s the deal:
If your data is pretty tight (like, low variability), then rounding could mess things up more. But if there’s a lot of natural fluctuation (like temps bouncing all over the place), then rounding might not change much. Still, why risk it?
Best move? Keep your 0.1°C data as-is for the actual stats and analysis. You can always round later when you’re showing results to someone who doesn’t need the fine-grained details.
If you’re really worried about fairness, you can even treat the public data like it represents a range—like if it says 22°C, maybe it actually means somewhere between 21.5 and 22.5°C. That way, you can still work with your full-precision data and just adjust how you interpret the comparison.
janedaww
·
2025-04-07T08:01:57Z (11 days ago)