And, for my taste, 25.68%±40.42 is really bad style. 26%±40 is the amount of precision I would accept here.
25.68% ± 40.42 is not the same as 26% ± 40. If I am later trying to make a model to get the best estimate of <stat> by looking at a bunch of other people's measurements I will get a more accurate answer if more digits are contained.
When someone presents a state like 26% without the ± it's at least excusable, since it's avoiding making people think that 25.68% was short hand for 26.68% ± (0.005 ± 0.005)%, but when we're including the ± there is no good reason to chop off digits.
I do like the comment someone else has about reporting this as a range instead of with a ± sign, i.e. "-14.74 to 66.1 percent".
Sure, it's still convenient when writing things out in text to not continue adding digits far beyond the uncertainty of your value, but even so there is nothing "conservative" or in any way accurate about chopping off a bunch of digits (however imprecise) and replacing them with zeros. This is especially clear if we consider that we generally only ever consider sig figs in base 10, but in any computational context that number is definitely not stored in base 10.
They aren't, but that's still the number you got. Chopping off the digits means you're slightly changing the result. It's like Tic Tac and their "0g of sugar" label, despite being 90% sugar.
No-one I've seen arguing for fewer significant digits says that a probability of 0.999 should be rounded to 1. Nor do they say it's ok to round 0.5 to 0.
If you got 10 decimal places in your calculation, it would obviously be nonsense to give all of them.
> If I am later trying to make a model to get the best estimate of <stat> by looking at a bunch of other people's measurements I will get a more accurate answer if more digits are contained.
But that is exactly what I doubt: that level of accuracy is not there. You predict a number that has been observed for a sample size of 35. If you now add sample #36, you will almost certainly get a different number. The (round-about, I did not calculate it in any way) two floating point digits are not accurate, they are chance. And a model predicting 25.72 would be no better than one predicting 25.94.
On the other hand, I think this study should be the poster child of "please don't use p-values" movement. Everything significant, but floats around zero.