12-15-2004, 09:06 PM | #2 (permalink) |
Psycho
Location: Atlanta, GA
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I think it's false, simply because it's a correlation thing not a cause and effect thing. Just because things correlate does not mean one causes the other.
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12-15-2004, 09:17 PM | #3 (permalink) |
Drifting
Administrator
Location: Windy City
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Shred's got it - r values only show the the correlations or relationships between two variables, and never, at ANY time, do they say that X actually causes Y.
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12-15-2004, 09:22 PM | #5 (permalink) |
Crazy
Location: U.S.A
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Definately false,
Correlations are measures of association and do not indicate cause and effect. I think this sort of question is on most early stat quizes/tests. A correct answer would be that it indicates a perfect inverse correlation between x and y. |
12-22-2004, 07:47 PM | #8 (permalink) |
Psycho
Location: Virginia Beach, VA
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False. "Correlation is not causation."
High correlation (even perfect correlation) proves nothing about causation. Both X and Y could be caused by an uncontrolled variable, yet have no direct connection between them. Even so, I think Bayesian statistics would give that statement a "mostly true".
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Simple Machines in Higher Dimensions |
12-23-2004, 11:20 PM | #9 (permalink) | |
Psycho
Location: Ouuuterrrr Spaaaaacccceeee
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12-23-2004, 11:45 PM | #10 (permalink) | |
Upright
Location: Chapel Hill, NC
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But, if you've got a theory: F=ma. Take a mass, apply a bunch of different F's to it. Measure the a's. Plot the results. Find that yes, indeed, 'F' is proportional to 'a' by a constant 'm' which turns out to be a property of the mass which we can also measure with a scale. The data supports the theory, and insofar as the theory is correct, F caused a. Strictly speaking, you can never prove a theory to be true beyond a shadow of a doubt, but you can demonstrate its validity to such a high degree that it would be silly to doubt the accuracy of the theory. |
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12-26-2004, 12:36 AM | #11 (permalink) |
Crazy
Location: U.S.A
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Statistical tests are not set up to prove that anything is true, rather they are set up to test the null hypothesis that some phenomena or event happened by chance, or that there are no differences in groups of subjects/events. They intend to disprove the null hypothesis.
A statistically significant result indicates evidence that the phenomena could not have happened by chance thus, we fail to reject the null hypothesis. A statistically significant pearson product moment correlation is merely saying that it is highly unlikely that the obtained correlation happened by chance. However, as many people have commented, correlational results do not imply causation because of the potential for spurious variables. For example, if one were to look at the correlation for per pupil expenditure money for each state in the US, and the academic achievement (SAT scores) of the students within each state, there is a inverse correlation between the amount per pupil expenditure and academic achievement (SAT scores) (More money = Less achievement). However, this correlation fails to take into account the percentage of students taking the SAT for each state. The states that spend the least per pupil have the fewest percentages of their students taking the exam and the states that spend the most per pupil have the highest percentages of students taking the SAT. Maybe in some states they only want the smart kids to take the SAT, maybe they don't have the expectation for all kids to go to college, so they don't push the SAT on students with lower academic achievement. All of which are valid questions. Therefore, after you statistically control for the percentage of students taking the SAT in each state, there is a positive correlation (as expected) between per pupil expenditure and SAT achievement. |
01-23-2005, 04:16 PM | #13 (permalink) |
Cracking the Whip
Location: Sexymama's arms...
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ah, r values.
That brings back some memories. Little did I know when I took sadistics that I would actually use them.
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01-26-2005, 08:09 AM | #15 (permalink) | |
Crazy
Location: U.S.A
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question, statistics |
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