billschnieder said:
Could you give me an example of a truly random method that could be used to select patients if those experiments knew nothing about any of the factors that affected the treatment?
I already gave you an example--just get a bunch of people who haven't received any treatment yet to volunteer for a study, then have a computer with a random number generator randomly assign each person to receive treatment A or treatment B. Do you agree that P(given person will be assigned by random number generator to receive treatment A) should be uncorrelated with P(given person will have some other background factor such as high socioeconomic status or large kidney stones)? If so, then the only reason group A might contain more people with a given factor (like large kidney stones) than group B would be a random statistical fluctuation, and the likelihood of any statistically significant difference in these background factors between group A and group B would get smaller and smaller the larger your sample size.
billschnieder said:
But they already did that. Their groups were already randomly selected according to them, they could very well have done it by use of a random number generator.
In the actual version of this study they weren't randomly selected. See the paradox wikipedia page[/url] where I think you got this example from (unless it also appears in other sources):
The sizes of the groups, which are combined when the lurking variable is ignored, are very different. Doctors tend to give the severe cases (large stones) the better treatment (A), and the milder cases (small stones) the inferior treatment (B). Therefore, the totals are dominated by groups three and two, and not by the two much smaller groups one and four.
In other words, they were sampling a group that had
already been assigned A or B by their doctors, and the likelihood that the doctor would assign them A was affected by the severity of their case, which was in turn affected by the size of their stones. So in this case, P(given person will be assigned by doctor to receive treatment A)
is correlated with P(given person will have background factor of large kidney stones). If the subjects were volunteers for a study who had not received any treatment, and their treatment was randomly assigned by a random number generator, then we expect P(given person will be assigned by doctor to receive treatment A) to be uncorrelated with P(given person will have background factor of large kidney stones). Of course the probability of an event differs from the frequency over a finite number of trials--if two people are flipping fair coins, we expect P(person #1 gets heads) to be uncorrelated with P(person #2 gets heads), i.e. P(person #1 gets heads, person #2 gets heads)=P(person #1 gets heads)*P(person #2 gets heads), but if there are only 4 trials the results might be HH, HH, HT, TT, in which case F(person #1 gets heads, person #2 gets heads) > F(person #1 gets heads)*F(person #2 gets heads), where F represents the frequency on those 4 trials. This is what I meant by a random statistical fluctuation, that the their can be a correlation in empirical frequencies even in situations where the probabilities should be uncorrelated. But again, the likelihood of a statistically significant correlation in frequencies in a scenario where the probabilities should be uncorrelated goes down the larger your sample size is.
billschnieder said:
What makes you think a computer will do any better, without taking into consideration the size of the stones.
Because there is no causal reason that the random number generator's likelihood of assigning a person to group A should be influenced by the size of someone's kidney stones (unlike with the case where doctors were deciding the treatment). So if we're using a random number generator to assign treatment, in the limit as the sample size goes to infinity, the fraction of people with large kidney stones in group A should approach equality with the fraction of people with large kidney stones in group B (and 'probability' is defined in terms of the frequency in the limit as the sample size goes to infinity, so this is why the probabilities are uncorrelated). With a finite sample size you might have a difference in the fractions for each group, but it could only be due to random statistical fluctuation.
billschnieder said:
You said Bell is calculating from the perspective of an omniscient being. But his inequalities are compared with what is obtained in actual experiments. I just gave you an example in which the results of the omniscient being were at odds with those of the experimenters, without any spooky action involved.
No you didn't. This is the key point you seem to be confused about:
the marginal correlation between treatment B and recovery observed by the omniscient being is exactly the same as that observed by the experimenters. The omniscient being does not disagree that those who receive treatment B have an 83% chance of recovery, and a person who receives treatment A has a 73% chance of recovery. All you are pointing out is that the omniscient being knows that this marginal correlation does not indicate a
causal relation between treatment B and recovery; the omniscient being knows that this correlation is related to the fact that doctors are more likely to assign patients treatment B if they have small kidney stones, and patients with small kidney stones are more likely to recover. (Alternately, if the patients were assigned to groups randomly and these numbers resulted, the omniscient being would know that the marginal correlation is just due to the fact that a random statistical fluctuation caused the number of patients with small kidney stones to differ significantly between the two groups)
Similarly, in a local hidden variables theory, an omniscient being knows that marginal correlations between different measurement outcomes don't represent a causal relation between the different measurements, but are in fact explained by the statistics of hidden variables which influence each measurement.
But just as above, the omniscient being sees exactly the same marginal correlation between measurements that's seen by the experimenters, so it's perfectly legitimate to use the omniscient being's perspective to derive some statistical rules that would apply to the marginal correlations under the assumption of local realism, then see if the actual statistics for marginal correlations seen by real experimenters obey those rules, and if they don't take that as a falsification of local realism.
billschnieder said:
That is your usual response, modifying my numbers, so that it is no longer the example I presented. You think I chose those numbers at random? Those specific numbers were chosen to illustrate the point. Do you deny the fact that in the numbers I gave, the omniscient being concludes that Treatment A is more effective, while the experimenters conclude that treament B is more effective? Surely you do not deny this.
No, I don't deny this, but when you say "the omniscient being concludes that treatment A is more effective", you are talking about the
causal relation between treatment A and recovery, not the marginal correlation between those two variables. Again, the omniscient being agrees completely with the experimentalists about the marginal correlation between the variables, he just doesn't think this demonstrates a causal link. And in Bell's argument, the Bell inequalities are just statements about the marginal correlations between different measurements, not about causal relations between them. This is why your analogy makes absolutely no sense as a criticism of Bell's argument. The omniscient being who knows about the hidden variables in a local realist universe should say exactly the same thing about marginal correlations between measurement outcomes as is seen by hypothetical experimenters in the same local realist universe. Do you disagree?
billschnieder said:
Your only relevant response is that maybe the groups were not really random. So I ask you to present a mechanism by which they can ensure that the groups are truly random if they do not know all the hidden factors.
If by "random" you mean the statistics seen in our small group accurately match the statistics seen in a larger population, with a sample size of 700 they most likely already do; if doctors have a higher probability of assigning treatment B to those with small kidney stones in our sample of 700, then doctors in the larger population probably do so at about the same rate. So if we looked at the entire population of patients receiving either treatment A or treatment B, the marginal correlation with recovery would likely be about the same: about 83% of all people receiving treatment B would recover, and about 73% of all people receiving treatment A would recover.
But more likely, by "random" you mean that all other variables like large vs. small kidney stones are evenly distributed between the population receiving treatment A and the population receiving treatment B, so that any difference in recovery indicates a
causal relation between treatment and recovery rate. If so, then again, my answer is twofold:
1. If you used a random number generator on a computer to assign patients treatment, in the limit as the number of patients approaches infinity, all other variables
would approach being evenly distributed in the two groups (i.e. the probabilities are uncorrelated), so any difference in a finite-sized group would just be a random statistical fluctuation, and the larger the sample size the smaller the likelihood of statistically significant fluctuations (see
law of large numbers)
2. In any case this issue is completely irrelevant to Bell's argument, because Bell is only looking at the marginal correlations between measurement outcomes themselves, he's
not claiming that these marginal correlations indicate a causal relation between the outcomes (quite the opposite in fact). In your example there is
no dispute between the omniscient being and the experimentalists about the marginal correlation between receiving treatment B and recovering, it's just that if the experimentalists foolishly conclude this indicates a causal link between B and recovery, the omniscient being knows they're wrong.
billschnieder said:
What are you talking about, did you read what I wrote? The experimenters randomly selected two groups of people with the disease and THEN gave treament A to one and treament B to the other, YET their results were at odds with those of the omniscient being!
OK sorry, once I looked up the wikipedia page on Simpson's paradox I assumed you were just taking the example from there, I neglected to reread your post and see that you specified that "the experimenters select the two groups according to their best understanding of what may be random". In this case, if they are using some random method like a random number generator on a computer, it should be true that in the limit as the sample size approaches infinity, the percentage of patients with small kidney stones in group A should approach perfect equality with the percentage of patients with small kidney stones in group B, and the fact that they are very different in the actual groups of 350 must be very unlikely statistical fluctuation, like if you had two groups of 350 coin flips and the first had 70 heads while the other had 300 heads. Do you disagree?
I suppose it could be true that the marginal correlations seen in actual Aspect-type experiments so far could differ wildly from the marginal correlations that an omniscient being would expect in the limit as the number of particle pairs sampled went to infinity. Still, this too should only be due to statistical fluctuations, and the
law of large numbers says that the more trials you do, the more probably it is that your measured statistics will be in close agreement with the expected probabilities under the same experimental conditions, with "probability" defined in terms of the statistics that would be seen in the limit as the number of trials approaches infinity. Again, do you disagree with this?
billschnieder said:
So? The example I presented clearly show you that the results obtained by the experimenters is at odds with that of the omniscient being. Do you deny that? It also clearly shows that the sampling by the experimenters is unfair with respect to the hidden elements of reality at play.Do you deny that?
If it's a part of your assumption that the patients are assigned randomly to different treatments, then I agree the marginal correlation in frequencies is very different than the marginal correlation in probabilities, i.e. the marginal correlation in frequencies that would be expected in the limit as the size of the sample went to infinity (with the experiment performed in exactly the same way in the larger sample, including the same random method of assigning patients treatments). But this sizeable difference would just be due to a freak statistical fluctuation--in fact we can calculate the odds, if we have 700 people and 357 have small kidney stones, and they are each randomly assigned to a group by a process whose probability of assigning someone to a group is independent of whether they have small kidney stones or not, then we can use the
hypergeometric distribution to calculate the probability that a group of 350 would contain 87 or less with small stones, or 270 or more. Using the calculator http://stattrek.com/Tables/Hypergeometric.aspx , with population size=700, sample size=350, and number of successes in population=357, you can see that if you plug in number of successes in sample=87, the probability of getting that many or fewer is 1.77*10^-45, just slightly higher than the probability of getting
exactly that many, which is 1.60013*10^-45; and similarly if number of successes in sample=270, the probability of getting exactly that many is also 1.60013*10^-45 (the calculator breaks down in calculating the probability of getting that many or more, but it should also be 1.77*10^-45). So under the assumption that the patients were assigned treatment by a random process whose probability of assigning A vs. B is in no way influenced by the size of a patient's kidney stones, you can see that the numbers in your example represent an
astronomically unlikely statistical fluctuation, and if the experiment were to be repeated with another group of 700 it's extremely probable the observed statistics would be a lot closer to the correct probabilities known by the omniscient being (and the law of large numbers says that the more times you repeat the experiment, the less likely a significant difference between true probabilities and observed statistics becomes).
JesseM said:
('correlation is not causation')
billschnieder said:
Tell that to Bell. He clearly defined "local causality" as lack of logical dependence.
No, he didn't define it as a lack of logical dependence in the
marginal correlations between measurement outcomes, only in the correlations conditioned on values of λ. The meaning of "correlation is not causation" is that
marginal correlations don't indicate causal dependence, and Bell didn't say they should, nor did he say that a
lack of causal influence between measurement outcomes would mean a lack of marginal correlations between them.
JesseM said:
Bell does not just assume that since there is a marginal correlation between the results of different measurements on a pair of particles, there must be a causal relation between the measurements; instead his whole argument is based on explicitly considering the possibility that this correlation would disappear when conditioned on other hidden variables
billschnieder said:
You are confused. Bell clearly states that logical dependence between A and (B or b), is not allowed nor is logical dependence between B and (a or A) allowed in his definition of "local causality".
You're the one who's confused here. In Bell's example there clearly
can be a marginal correlation (logical dependence) between A and B; in fact Bell's
original paper dealt with the simplest case where if you just looked at the measurement results when both experimenters chose the same detector setting, there was a perfect anticorrelation between the results (read the first paragraph of the 'Formulation' section). Bell is just saying that the correlation disappears when you condition on any specific value of the variable λ.