KONY said:
Very true! For instance, in the info Antsi gave us, we not given a sample size nor an effect size coefficient (we know the direction, but not magnitude). Both are necessary whenever you report the relationships between variables. Not saying this is junk science but just giving an example.
It is not conventional to report effect sizes for nonsignificant relationships. That would be like saying "there is no unicorn, and the unicorn weighs one thousand pounds." [I did admittedly somewhat violate this logic by citing the positive sign, a bit like saying "there is no unicorn, and even if there were, it would be white."]
I assumed that most people know how many states there are in the U.S., and I wasn't really intending to write this up to scientific publication standards. But, if it will make you happier, (n = 50). To really do the descriptives justice, I should probably plot a frequency distribution of the Brady rankings and the FBI violent crime stats, but honestly, I'm getting a little tired of this.
Your bringing in measurement error is a red herring here. Any statistical test always assumes reliability of measures. This is a limitation of both univariate and multivariate statistical models, not a limitation particular to Pearson correlations.
Earlier, you were talking about moderating variables accounting for a causal relationship between a noncorrelated predictor and criterion. (For what it's worth, that's what I was asking for an example of. I understand the concept of measurement error - the concept you are proposing that I'm having a hard time with is noncorrelated causation). Now, you're retreating into a discussion of measurement error and statistical power - which are limitations of any statistical argument, and as I pointed out, not really in play in this example.
I will restate my assertion: "Given reliable measures and appropriate statistical power (which are always assumptions of any statistical test), the absence of a correlation constitutes strong evidence against a causal relationship."
This does argue on the side of what gun owners usually assert - namely, that crime is caused by other factors than those addressed by gun control laws. I tabulated the crime data and the Brady rankings, and found no correlation. This is evidence against the assertions of the Bradyites, namely, that the laws they advocate will cause lower crime rates.