Kasz216 said:
Or honestly... hell
http://www.socialresearchmethods.net/kb/statcorr.php
Null Hypothesis: r = 0
Alternative Hypothesis: r <> 0
The easiest way to test this hypothesis is to find a statistics book that has a table of critical values of r. Most introductory statistics texts would have a table like this. As in all hypothesis testing, you need to first determine the significance level. Here, I'll use the common significance level of alpha = .05. This means that I am conducting a test where the odds that the correlation is a chance occurrence is no more than 5 out of 100. Before I look up the critical value in a table I also have to compute the degrees of freedom or df. The df is simply equal to N-2 or, in this example, is 20-2 = 18. Finally, I have to decide whether I am doing a one-tailed or two-tailed test. In this example, since I have no strong prior theory to suggest whether the relationship between height and self esteem would be positive or negative, I'll opt for the two-tailed test. With these three pieces of information -- the significance level (alpha = .05)), degrees of freedom (df = 18), and type of test (two-tailed) -- I can now test the significance of the correlation I found. When I look up this value in the handy little table at the back of my statistics book I find that the critical value is .4438. This means that if my correlation is greater than .4438 or less than -.4438 (remember, this is a two-tailed test) I can conclude that the odds are less than 5 out of 100 that this is a chance occurrence. Since my correlation 0f .73 is actually quite a bit higher, I conclude that it is not a chance finding and that the correlation is "statistically significant" (given the parameters of the test). I can reject the null hypothesis and accept the alternative.
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This is a statistical inference test for a graph. The null hypothesis states that there is no correlation, whereas the alternative hypothesis states that there is. You seem to think that alpha levels are only used in conjunction with r-values, however the point i'm trying to make is that alpha values for determining statistical significance (the original segue for the whole 'disagreement') do not need to have an accompying r-val. An example? Let's say that i have to test data from a sample survey that talks about proportions of married men. You would use an alpha-level in your statistical inference to test the probability of obtaining a sample such as this.
I know I'm right, i'm not sure why i keep responding. -_-
