Everything Is Correlated - https://news.ycombinator.com/item?id=19797844 - May 2019 (53 comments)
> Since every piece of matter in the Universe is in some way affected by every other piece of matter in the Universe, it is in theory possible to extrapolate the whole of creation — every sun, every planet, their orbits, their composition and their economic and social history from, say, one small piece of fairy cake.
Given different T_zero configs of matter and energies T_current would be different. and there are many pathways that could lead to same physical configuration (position + energies etc) with different (Universe minus cake) configurations.
Also we are assuming there is no non-deterministic processed happening at all.
After all, Feynman showed this is in principle possible, even with local nondeterminism.
(this being a text medium with a high probability of another commenter misunderstanding my intent, I must end this with a note that I am, of course, BSing :)
Why? We learn about the past by looking at the present all the time. We also learn about the future by looking at the present.
> Also we are assuming there is no non-deterministic processed happening at all.
Depends on the kind of non-determinism. If there's randomness, you 'just' deal with probability distributions instead. Since you have measurement error anyway, you need to do that anyway.
There are other forms of non-determinism, of course.
> Bohm employed the hologram as a means of characterising implicate order, noting that each region of a photographic plate in which a hologram is observable contains within it the whole three-dimensional image, which can be viewed from a range of perspectives.
> That is, each region contains a whole and undivided image.
> "There is the germ of a new notion of order here. This order is not to be understood solely in terms of a regular arrangement of objects (e.g., in rows) or as a regular arrangement of events (e.g., in a series). Rather, a total order is contained, in some implicit sense, in each region of space and time."
> "Now, the word 'implicit' is based on the verb 'to implicate'. This means 'to fold inward' ... so we may be led to explore the notion that in some sense each region contains a total structure 'enfolded' within it."
No?
You can have two independent random walks. Eg flip a coin, gain a dollar or lose a dollar. Do that to times in parallel. Your two account balances will change over time, but they won't be correlated.
This, once developed, just happened to be a useful method. But given the abuse using those methods, and the proliferation of stupidity disguised as intelligence, it's always fitting to question it, and this time with this correlation noise observation.
Logic, fundamental knowledge about domains, you need that first. Just counting things without understanding them in at least one or two other ways, is a tempting invitation for misleading conclusions.
I cannot see the problem in that. To get to meaningful results we often calculate with simplyfied models - which are known to be false in a strict sense. We use Newtons laws - we analyze electric networks based on simplifications - a bank-year used to be 360 days! Works well.
What did i miss?
You didn't really miss anything. The article is incomplete, and wrongly suggests that something like "false" even exists in statistics. But really something is only false "with a x% probability of it actually being true nonetheless". Meaning that you have to "statistic harder" if you want to get x down. Usually the best way to do that is to increase the number of tries/samples N. What the article gets completely wrong is that for sufficiently large N, you don't have to care anymore, and might as well use false/true as absolutes, because you pass the threshold of "will happen once within the lifetime of a bazillion universes" or something.
Problem is, of course, that lots and lots of statistics are done with a low N. Social sciences, medicine, and economy are necessarily always in the very-low-N range, and therefore always have problematic statistics. And try to "statistic harder" without being able to increase N, thereby just massaging their numbers enough to get a desired conclusion proved. Or just increase N a little, claiming to have escaped the low-N-problem.
I do not think it is accurate to portray the author as someone who does not understand asymptotic statistics.
For example, eat a lot and you will gain weight, gain weight and you will feel more hungry and will likely eat more.
Or exercise more and it becomes easier to exercise.
Earning money becomes easier as you have more money.
Public speaking becomes easier as you do it more and the more you do it, the easier it becomes.
Etc...
That's saying the same thing twice :)
Only if you don't injure yourself while exercising.
But I suspect that being able to figure out causation doesn't matter much from a survival or reproduction perspective because cause and effect are just labels.
Reality in a self-perpetuating cycle is probably like Condition A is 70% responsible and Condition B is 30% responsible for a problem but they feedback and exacerbate each other... You could argue that Condition A is the cause and Condition B is the effect because B < A but that's not quite right IMO. Also, it's not quite right to say that because A happened first, that A is the cause of a severe problem... The problem would never have gotten so bad to such extent without feedback from B.
Wait. Sir Arthur Conan Doyle lived at basically the exact same time as this Karl Pearson.
Is that why the Sherlock Holmes stories had handwriting analysis so frequently? Was there just pop science going around at the time that like, let's find correlations between anything and anything, and we can see that a criminal mastermind like Moriarty would certainly cross their T's this way and not that way?
Please explain.
People interpret "statistically significant" to mean "notable"/"meaningful". I detected a difference, and statistics say that it matters. That's the wrong way to think about things.
Significance testing only tells you the probability that the measured difference is a "good measurement". With a certain degree of confidence, you can say "the difference exists as measured".
Whether the measured difference is significant in the sense of "meaningful" is a value judgement that we / stakeholders should impose on top of that, usually based on the magnitude of the measured difference, not the statistical significance.
It sounds obvious, but this is one of the most common fallacies I observe in industry and a lot of science.
For example: "This intervention causes an uplift in [metric] with p<0.001. High statistical significance! The uplift: 0.000001%." Meaningful? Probably not.
And if we increase N enough we will be able to find these 'good measurements' and 'statistically significant differences' everywhere.
Worse still if we did not agree in advance what hypotheses we were testing, and go looking back through historical data to find 'statistically significant' correlations.
Significance does not tell you this. Significance can be arbitrarily close to 0 while the probability of the null hypothesis being true is simultaneously arbitrarily close to one
This is such a bizarre sentence. The way its tossed in, not explained in any way, not supported by references, etc. Like I guess the implication being made is something like "because there is a hidden latent variable that determines criminality and we can never escape from correlations with it, its ok to use "is_black" in our black box model which decides if someone is going to get parole? Ridiculous. Does this really "throw doubt" on whether we should care about this?
The concerns about how models work are deeper than the statistical challenges of creating or interpreting them. For one thing, all the degrees of freedom we include in our model selection process allow us to construct models which do anything that we want. If we see a parole model which includes "likes_hiphop" as an explanatory variable we ought to ask ourselves who decided that should be there and whether there was an agenda at play beyond "producing the best model possible."
These concerns about everything being correlated actually warrant much more careful understanding about the political ramifications of how and what we choose to model and based on which variables, because they tell us that in almost any non-trivial case a model is at least partly necessarily a political object almost certainly consciously or subconsciously decorated with some conception of how the world is or ought to be explained.
It reads naturally in context and is explained by the foregoing text. For example, the phrase "these theoretical & empirical considerations" refers to theoretical and empirical considerations described above. The basic idea is that, because everything correlates with everything else, you can't just look at correlations and infer that they're more than incidental. The political implications are not at all "weird", and follow naturally. The author observes that social scientists build complex models and observe huge amounts of variables, which allows them to find correlations that support their hypothesis; but these correlations, exactly because they can be found everywhere, are not anywhere near as solid evidence as they are presented as being.
> Like I guess the implication being made is something like "because there is a hidden latent variable that determines criminality and we can never escape from correlations with it, its ok to use "is_black" in our black box model which decides if someone is going to get parole?
No, not at all. The implication is that we cannot conclude that the black box model actually has an "is_black" variable, even if it is observed to have disparate impact on black people.
Nothing in the statistical observation that variables tend to be correlated suggests we should somehow reject the moral perspective that that its desirable for a model to be based on causal rather than merely correlated variables, even if finding such variables is difficult or even, impossible to do perfectly. And its certainly also _meaningful_ to do so, even if there are statistical challenges. A model based on "socioeconomic status" has a totally different social meaning than one based on race, even if we cannot fully disentangle the two statistically. He is mixing up statistical and social, moral and even philosophical questions in a way which is, in my opinion, misleading.
eisvogel•6h ago