except in real experimentation you control for all the variables except the one you're investigating. So it is not a very usefull thing to compare a bulb with the Sun.

Instead, if you state that "luminescence OF THE 60W INCANDESCENT LIGHTBULB is 400-1200 lumens with 95% CI", then that's the usefull information that let's you set the right expectations when designing lightning for your new house, for example.

I think you missed the point of my example. I was suggesting that an experiment performed on the sun showing a 95% CI of the brightness being 400-1200 lumens should result in a reasonable person believing that the probability of the Sun's brightness falling in that range is approximately zero, while the same result for a 60W light-bulb should result in a reasonable person being more than 95% certain that the bulb's brightness falls in that range.

Just like a large number of people misinterpret a P value of .01 to mean a 1% chance of the results being due to chance[1], CIs can be similarly misinterpreted.

1: A .01 P value actually means that if the null hypothesis is true, then you would get the result 1% of the time. The analogy to my above example would be that if I run an experiment and get a result that "the sun is less bright than a 60W light-bulb" with a P value of .01, it's almost certainly not true that the sun is less bright than the light-bulb, since the prior probability of the sun being less bright than a 60W light bulb is many orders of magnitude smaller than 1%.