I consider all the things you’ve said to be my best arguments why you’re wrong, so there’s clearly something wrong here. But I’ve run out of novel arguments and can’t figure out where the disconnect is.
You seem to think that it is generally easy to turn arbitrary ambiguities into numbers in a way amenable to using statistics to resolve them. I find that to be obviously, blatantly false.
Where you see things like this:
It’s pretty easy to construct estimates. The problem is that without good data these estimates will be too wide to the point of uselessness. But you can think, and find some data, and clean some existing data, and maybe narrow these estimates down a bit. Go back to 1. and repeat until you run out of data or the estimate is narrow enough to fit its purpose.
I see something more like
In order to get an estimate narrow enough to fit the purpose, gather data, make a bad estimate, gather more data, refine the estimate, gather still more data, refine further, repeat until you can’t find any more data and then hope you got something useful out of it.
Where the difficult part is gather data. If you can gather data that is relevant, then statistics are useful. But often, you can’t, and so they aren’t. I outlined the exact same process as you, I’m just significantly more pessimistic about how often and how well it works.
You seem to think that it is generally easy to turn arbitrary ambiguities into numbers
Yes, I do.
in a way amenable to using statistics to resolve them.
No, I do not. I said nothing about “resolving” things.
When I say “numbers” in the context of statistics, I really mean probability distributions, often uncertain probability distributions. For example, the probability of anything lies somewhere between zero and one—see, we don’t have any information, but we already have numbers.
You’re likely thinking that when I am turning ambiguities into numbers, I turn them into nice hard scalars, like “the probability of X is 0.7”. No, I don’t. I turn them into wide probability distributions, often without any claims about the shape of these distributions. That is still firmly within the purview of statistics.
Where the difficult part is gather data. If you can gather data that is relevant, then statistics are useful.
If you have no data, nothing is useful. Remember, the original context was how humanities teach us to deal with ambiguity. But if you have no data, humanities won’t help and if you do, you can use numbers.
I’m not saying that everything should be converted to numbers. My point is that there are disciplines—specifically statistics—that are designed to deal with uncertainty and, arguably, do it better than handwaving common in the humanities.
I consider all the things you’ve said to be my best arguments why you’re wrong, so there’s clearly something wrong here. But I’ve run out of novel arguments and can’t figure out where the disconnect is.
What is that statement of mine to which you are assigning the not-true value?
You seem to think that it is generally easy to turn arbitrary ambiguities into numbers in a way amenable to using statistics to resolve them. I find that to be obviously, blatantly false.
Where you see things like this:
I see something more like
Where the difficult part is gather data. If you can gather data that is relevant, then statistics are useful. But often, you can’t, and so they aren’t. I outlined the exact same process as you, I’m just significantly more pessimistic about how often and how well it works.
Yes, I do.
No, I do not. I said nothing about “resolving” things.
When I say “numbers” in the context of statistics, I really mean probability distributions, often uncertain probability distributions. For example, the probability of anything lies somewhere between zero and one—see, we don’t have any information, but we already have numbers.
You’re likely thinking that when I am turning ambiguities into numbers, I turn them into nice hard scalars, like “the probability of X is 0.7”. No, I don’t. I turn them into wide probability distributions, often without any claims about the shape of these distributions. That is still firmly within the purview of statistics.
If you have no data, nothing is useful. Remember, the original context was how humanities teach us to deal with ambiguity. But if you have no data, humanities won’t help and if you do, you can use numbers.
I’m not saying that everything should be converted to numbers. My point is that there are disciplines—specifically statistics—that are designed to deal with uncertainty and, arguably, do it better than handwaving common in the humanities.
Your confidence in your ability to do statistics to everything is clearly unassailable, and I have no desire to be strawmanned further.