Precognition paper to be published in mainstream journalby pftest Tags: journal, mainstream, paper, precognition, published 

#37
Nov1910, 06:29 PM

P: 3,390

The test itself, as per the article had 50/50 odds of the test subject guessing correctly. So I don't see 53/47 as being statistically amazing. EDIT: I'm talking in regards to prediction so far as the coin toss odds. The 53% must be from another experiment. The first one in the article I believe. 



#38
Nov1910, 06:54 PM

P: 3,390

Perhaps I should elaborate.
By always having a 50/50 chance of any outcome. No matter what you predict the odds of it occurring are the same. Any pattern you choose so far as a coin toss goes is equally likely to occur. So you really need to shift the odds to >70/30 to show strong predictability. I'd prefer a test with smaller odds, say 1 in 6, of you guessing the result. That way you have significant odds against you simply guessing on each turn. By using 50/50 you are swinging the odds in favour of a guess. Even a roll of the dice, giving the 1 in 6 odds, gives an even chance of any pattern occurring. However, it does mean that there is a 5 in 6 chance you are wrong on each go, making a string of correct predictions far more spectacular and significantly less likely. 



#39
Nov1910, 08:20 PM

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It's not the same. Let's take a 2 coin toss experiment to start. There are four possibilities. H H H T * T H * T T Only one possibility out of 4 gives you all heads. That's one chance in 4. But there there are two possibilities that given you equal number of heads and tails, H T and T H. So the probability to tossing equal number of heads vs. tails is 50% or one chance in two attempts. Moving on to a experiment with 4 tosses, H H H H H H H T H H T H H H T T * H T H H H T H T * H T T H * H T T T T H H H T H H T * T H T H * T H T T T T H H * T T H T T T T H T T T T There are 16 possible outcomes and only 1 with all heads. So there is one chance in 16 of getting all heads. But there are 6 ways of getting an equal number of heads and tails. So the probability of equal heads and tails is 6/16 = 37.5% or about one chance in 2.67 attempts. It turns out that one can calculate the number of ways to produce an outcome of the coin toss flip using [tex] \left( \begin{array}{c}n \\ x \end{array} \right) = \frac{n!}{x!(nx)!} [/tex] where n is the number of tosses, and x is the number of heads (or tails). So for a 10toss experiment, the chances of getting all heads is 1 in 1024, but the chances of getting equal number of heads and tails is 24.6094% or about 1 in 4. But if you don't care which coins come up heads as long as there is an even number of heads and tails, things are very different. The experiments presented in the paper don't really care which order the words are recalled, or which specific words happen to be in the "practice" or "control" set. The experiments are not looking for overly specific patters, they are looking for sums of choices that are statistically unlikely when taken as a whole. But that is not the case for rolling the die twice and taking the sum. Or, the same thing, guessing on the sum of two dice rolled together. If you were to guess on the sum being 2 (snake eyes), you have a 1 chance in 36 On the other hand, if you were to guess that the sum is 7, your odds are incredibly better. There are 6 combinations that give you a score of 7. That makes your odds 6/36 = 16.6667% or 1 chance in 6. [Edit: fixed a math/typo error.] [Another edit: Sorry if this is a bit off topic but this subject is fascinating. It's a curious aspect of nature that things tend to reach a state of equilibrium. At the heart of nature, this aspect is because there are a far greater number of possible states that are roughly equally distributed and far fewer states at the extremes. At submicroscopic scales, there's really no such thing as friction and all collisions are essentially elastic and reversible. But when considering groups of atoms and particles taken together, there are far more states that have roughly equal distribution and far fewer at extreme situations, all else the same (such as the total energy being the same in all possible states). it's this property that we are talking about here that explain friction, inelastic collisions, nonconservative forces, and the second law of thermodynamics when scaled up to macroscopic scales. And perhaps most importantly, the reason that getting 5 heads in a 10toss coin experiment is far more likely than getting 10 heads is essentially the same reason why my coffee cools down on its own instead of heating up spontaneously.] 



#40
Nov1910, 09:21 PM

P: 3,390

Yes, I was referring to predicting a specific pattern.
As per another thread, probability isn't my strong suit. A very interesting post from you there and I thank you. Cleared up some other questions I had as well. 



#41
Nov1910, 11:00 PM

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P: 735

It's got to be this one (well reasoned opinion). Frankly, I think it's because the tests are fundamentally noncausal (i.e. don't take place during forward propagation on the positive taxis). You can never remove the systematic bias from the test: the data point is always taken before the test is performed. I don't mean that in a trivial "oh, that's neat" way. Seriously consider it. The data being taken in a "precognitive memorization test" is taken prior to the test being performed. 1)Memorize words 2)Recall words test 3)Record results 4)Perform typing test So we have a fundamental problem. This is situation in which one of the following two scenarios MUST be true: 1) Either the list of words to be typed during the typing test are generated PRIOR to the recall test, or 2) the list of words to be typed during the typing test are generated AFTER the recall test. In the case of (1), it would be impossible to separate precognition from remote viewing. In the case of (2), there is a tiny chance that the event is actually causal (in that the generation process could be influenced by the results of the recalled word test). (For the purposes of this problem description I am assuming that causal events are more likely than noncausal events.) 



#42
Nov1910, 11:08 PM

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The study paper says in the experiment, "Experiment 1: Precognitive Detection of Erotic Stimuli," that there were 100 participants. 40 of the participants were shown each 12 erotic images (among other images), and the other 60 participants were each shown 18 erotic images (among others). That makes the total number of erotic images shown altogether, (40)(12)+ (60)(18) = 1560 erotic images shown. The paper goes on to say, However, after reading that, it's not clear to me whether the 53.1% is the total hit rate averaged across all total erotic pictures from all participants, or whether that is the average eroticimage hit rate of each participant. I don't think it matters much, but I'm going to interpret it the former way, meaning a hit rate of 53.1% of the total 1560 erotic images shown. So this is sort of like a 1560toss coin experiment. 53.1% of 1560 is ~828. So I'm guessing that the average number of "correct" guesses is 828 out of 1560 (making the percentage more like 53.0769%). We could use the binomial distribution [tex] P(nN) = \left( \begin{array}{c}N \\ n \end{array} \right) p^n (1p)^{(Nn)} = \frac{N!}{n!(Nn)!} p^n (1p)^{(Nn)} [/tex] Where N = 1560, n = 828, and p = 0.5. But that would give us the probability of getting exactly 828 heads out of 1560 coin tosses. But we're really interested in finding the probability of getting 828 heads or greater, out of 1560 coin tosses. So we have to take that into consideration too, and our equation becomes, [tex] P = \sum_{k = n}^N \left( \begin{array}{c}N \\ k \end{array} \right) p^k (1p)^{(Nk)} = \sum_{k = n}^N \frac{N!}{k!(Nk)!} p^k (1p)^{(Nk)} [/tex] Rather than break my calculator and sanity, I just plopped the following into WolframAlpha: "sum(k=828 to 1560, binomial(1560,k)*0.5^k*(10.5)^(1560k))"Thank goodness for WolframAlpha. (http://www.wolframalpha.com) The results are the probability is 0.00806697 (roughly 0.8%) That means the probability of 53.1% heads or better in 1560toss coin experiment, merely by chance with a fair coin, is 1 in 124. Similarly, the chances of the participants randomly choosing the "correct" side of the screen in erotic image precognition test 53.1% or better, on average, on the first experiment (with all 100 subjects choosing which side 12 or 18 times each), merely by chance, is 1 out of 124. I'd call that statistically significant. 



#43
Nov1910, 11:49 PM

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P: 735

In fact, and I could be wrong, I understood it to mean that the options were always "left" or "right" but that not every left=right set contained a possible correct answer. I think I'll have to read again. 



#44
Jan911, 03:39 AM

P: 271

A story on daryl bem's paper in the new york times:
Another quote: 



#45
Jan911, 08:58 AM

P: 2,281

Forget the article and focus on the actual paper, which is a different matter. Beyond that, you need to learn what the scientific method is so you can understand when you posit that null hypothesis, and why. Nobody here should have to argue with you, just to realize that you need further education on the subject. For instance, would it be logical to assume the existence (i.e. truth of hypothesis) of something, then go about to prove your assumption? That's called... NOT SCIENCE... in fact it's enough to end your career regardless of the research subject. To pass off the results of a test designed to exploit a known neurological process is just... stupid. There's something to be examined here, but IF it's repeatable, then it doesn't sound ESPy to me at all. This is ESP in the way that forgetting where your keys are, then suddenly having an idea in your mind that they're under couch! You must be psychic, and all because of your mindset while waiting for your search pattern to improve based on dim memory. 



#46
Jan911, 10:26 AM

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#47
Jan911, 10:35 AM

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#48
Jan911, 11:22 AM

P: 771

Here is a PDF of a response paper:
http://dl.dropbox.com/u/1018886/Bem6.pdf It looks like there are some serious flaws with the ESP paper. The one I have the biggest problem with is coming up with a hypothesis from a set of data, and then using that same set of data to test the hypothesis. It's a version of the Texas Sharpshooter Fallacy. Here's what the paper I linked has to say, in part, on this matter: 



#49
Jan911, 11:28 AM

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#50
Jan911, 12:24 PM

Other Sci
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P: 1,342

Perhaps this falls into the category of "journalism" that seems so despised in this discussion, but Jonah Lehrer wrote a nice article for The New Yorker that touches on issues relevant to the debate (similar to the points already brought up in the thread: that subtle biases in study design, analysis and interpretation can introduce significant biases and lead to erroneous results). In particular, he talks about some work done by Jonathan Schooler:
In essence, Schooler replicated the results of the Bem paper but, after performing many more tests, showed that the results were noting but a statistical anomaly. I'm not aware whether Schooler published these results. This, especially in light of other such examples detailed in Lehrer's piece, is why I'm hesitant to trust findings based primarily on statistical data without a plausible, empiricallytested mechanism explaining the results. 



#51
Jan911, 06:05 PM

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#52
Jan911, 11:40 PM

P: 271

Perhaps you didnt read it because it now requires a login (it didnt when i posted it yesterday), but registration is free. 



#53
Jan1011, 06:22 AM

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#54
Jan1011, 07:48 AM

P: 3,178

PS as a personal anecdote, as a kid I once came across a "onearmed bandit" gambling machine with a group of guys around it. They had thrown a lot of false coins(!) in the machine and one of them was about to throw in the last coin when he noticed me. After I confirmed to him that I had never gambled before he asked me to throw it in, and I got jackpot for them  most of it consisting of their own false coins. I left the scene with mixed feelings, as they had robbed my chance on beginners luck for myself... 


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