= 27d567bc2d48d9f40e9ccc20ea370b15)
Lumifer comments on Rationality Quotes May 2014 - Less Wrong
You are viewing a comment permalink. View the original post to see all comments and the full post content.
= 783df68a0f980790206b9ea87794c5b6)
Loading…= b715d02e85f7b3b4ae65ca3d3e450868)
Subscribe to RSS Feed
Powered by Reddit
= f037147d6e6c911a85753b9abdedda8d)
You are viewing a comment permalink. View the original post to see all comments and the full post content.
Comments (294)
That phrase is so general as to be pretty meaningless.
I do not subscribe to the notion that anything not expressible in math is worthless, but "in most circumstances" the inability to find any numbers is a strong indication that you don't understand the issue well.
Yes, that's the whole point. There aren't always numbers you can find, even when there are, finding them is nontrivial, and you often have to deal with the ambiguous situation or problem regardless.
What you said here is a vast oversimplification; if you have gotten to the point where you can find relevant numbers, you have already successfully navigated most of the ambiguity.
Is there still an inferential gap here? I thought I made my point clear about three comments ago, but this is clearly not as obvious a distinction as I expected it to be.
And that's where you are being misled by your insistence on "finding" numbers instead of "making" them.
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.
Ambiguity isn't some magical concept limited to the humanities. The whole of statistics is dedicated to dealing with ambiguity. In fact, my standard definition of statistics is "a toolbox of methods to deal with uncertainty".
I understand your point, I just think it's mistaken.
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.