I generally assume that more intelligent people than me would have stumbled onto any connection that obvious. So, I always take it as a warning sign that I've messed up a parameter or put a proxy variable into my training data if I see things reaching that level of effect.
If you assume that it's your calculation errors that lead to observed discrepancies (in eg. speed of light), you won't be able to measure speed of light until you can get those errors below the observation threshold.
If you don't even assume that you can get those errors below the threshold, you won't try and you will live happily in belief that the speed of light is, well, infinite.
The first measurement of the speed of light was too low. Subsequent experimenters assumed that when their measurement differed from the previous measurement by too much, then their experiment was wrong. So they fiddled with it until it wasn't too far off the previous value, and then published.
EDIT: it was about the charge on an electron, not the speed of light, my mistake: https://scipython.com/blog/measurements-of-the-electron-char...