TGGP: Eliezer referenced the book (the wikipedia url on the “real” link, lookup for the phrase “Is Idang Alibi about to take a position on the real heart of the uproar?”). I thought everybody followed the links before commenting ;). Anyway I assume that if something is referenced its discussion is on topic.
Regarding their data, we can’t just remove the data they fudged, we need to redo the analysis with the original data. We can’t just discard data because it doesn’t fit our conclusions. Using their raw data without fudging we are left with low correlation, many data points outside the curve.
Ditto for any other studies. I highly skeptical of sociologists or psychologist papers because they always (again IME) have use very bad statistics. Most assume a gaussian or poisson distribution without even proving that the process generating the data has the right properties. The measurement process is highly subjective and there’s no analysis to assess the deviance of individual measures, so they don’t properly find the actual stddev of their data. If one wants to aggregate studies, first one must prove that the measurement process for each study is the same (in the studies mentioned in your “predictive power” link this is false: at least two Lynn studies use population samples with different properties, also another couple use different IQ tests) otherwise we are mixing unrelated hypothesis.
I’m highly skeptical of IQ measurement, because it’s too subjective. Measuring the same individual over and over on a long interval we get different results, but we shouldn’t. A physicist wouldn’t use a mass measurement process that depended on subjective factors (e.g. if the measured object is pretty or the time of measurement isn’t jinxed), in a similar way we shouldn’t use a measure of mental capacity that is highly dependent of stress (which has no objective measurement process) or emotional state. In this situation one of the best approaches would be using many different data measurements for each individual and aggregate the data with Monte Carlo analysis to find the probability of each results. We can’t just fudge the data, discard sample we don’t like and use a subjective methodology, otherwise it isn’t science. When a physicist does a experiment he has a theory in mind, so he either already has an equation or ends up discovering one. The equation must account for all variables and the theory must prove why the other variables (e.g. speed of wind in Peking) doesn’t matter. “IQ and the Wealth of Nations” fails to prove that any other factors influencing GDP are irrelevant to the IQ correlation, that alone discredits the results.
Correlation is the most overused statistical tool. It is useful to show patterns but unless you have a theory to explain the results and make actual predictions it’s irrelevant as much as the scientific method is concerned. If we ignore this anything can be “proven”.
I’ve been reading these last posts on Science vs. Bayes and I really don’t get it. I mean, obviously bayesian reasoning supersedes falsifiability and how to analyze evidence, but there’s no conflict. Like relativity vs. newtonian mechanics, there’s thresholds that we need to cross to see the failures in Science, but there are many situations when just Science works effectively.
The New Scientist is even worse, the idea that we need to ditch falsifiability and use Bayes is idiotic, it’s like saying that binary logic should be discarded because we can use probabilities instead of zero and one. Falsifiability is a special case of Bayes, we can’t have Bayes without falsifiability (as we can’t have natural’s addition ruling out 2+2=4), the people that argue this don’t understand the extents of Bayes.
WRT multiverse IMHO we have to separate the interpretation of some theory from the theory itself. If the theory (which is testable, falsifiable, etc.) holds against the evidence and one of it’s results is the existence of a multiverse, then we have to accept the existence of the multiverse. If it isn’t one of the results, but it is one possible interpretation of how the theory “really works”, then we are in the realm of philosophy and we can spend thousands of years arguing any way without going forward. In most cases of QM theories there’s no clear separation of both, so people attach themselves to the interpretations instead of using the results. If we have two hypothesis that explain the same phenomena we have three possible choices:
they’re equal up to isomorphism (which means that doesn’t matter which one we choose, other than convenience).
one is simpler than the other (using whatever criteria of complexity we want to use).
both explain more than the phenomena.
Number 1 is a no-brainer. Number 3 is the most usual situation, where the evidence points either way and new evidence is necessary to confirm in both directions. We can use Bayes to assess the probability of each one being “the right one”, but if both theories don’t contradict each other then there’s a smaller theory inside each that falls in the case number 1. Number 2 is the most problematic because plain use of complexity assessment doesn’t guarantee that we are picking the right one. The problem lies in the evidence available: there’s no way to know if we have sufficient evidence to rule out any one. Just because a equation is simpler it doesn’t mean it’s correct, perhaps our data set is well known. Again it should be the cause that the simpler theory is isomorphic to a subset of the larger theory.
The only argument that needs to be spoken is if the multiverse is a result or an interpretation, but in the strictest sense of the word: we can’t say it’s an interpretation assuming that X and Y holds, because them it’s an interpretation of QM + X + Y. AFAIK every “interpretation” of QM extends the assumptions in a particular direction. Personally I find the multiverse interpretation cleaner, mathematically simpler and I would bet my money on it.
On your points of departure: (1) Shows how problematic academia is. I think the academic model is a dead end, we should value rationality more than quantity of papers published, the whole politics of the thing is way too much inefficient. (2) It won’t be enough because our culture values rationality much less than anything else. Even without bayesian reasoning plain old Science rules out the bible, you can either believe in logic or the bible. One of the best calculus professors I had was a fervent adventist. IMO our best strategy is just outsmart the irrationalists, our method is proven and yields much better results, we just need to keep compounding it to the singularity ;) (3) You’re dead wrong (in the example). There are many other necessary experiments other than seeing an apple fall to realize special relativity. Actually a bayesian super-intelligence could get trapped in local maximum for a long time until the “right” set of experiments happened. We have a history of successes in science but there’s a long list of known failures, let alone the unknown failures.