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OOXML Vote and the CPI Corruption Index
Tapani Tarvainen writes "It turns out there's an interesting correlation between Transparency International's 'corruption perceptions index' and voting behavior in ISO's OOXML decision. Countries with a lower score (more corruption) on the 2006 CPI were more likely to vote in favor of OOXML, and those with a higher score were less likely. According to the analysis, 'This statistics supports with a P value of 0.07328 the hypothesis that the corrupted countries were more likely to vote for approval (one-tailed Fisher's Exact test). In other words, simplified a bit: the likelihood that there was no positive correlation between the corruption level and probability of an approval vote, that is, this is just a random effect, is about 7%.' Of course, correlation doesn't prove causality."
Not only is 0.07 not significant, they used a 1-tailed test, rather than a 2-tailed test. If they had used the 2-tailed test, the p-value would have been 0.14, which is REALLY not significant. You're only ever justified in choosing the 1-tailed test over the 2-tailed one if you know for certain which way the influence is pushing. If, for example, one could make the case that the OOXML vote would have gone the other direction, with the more corrupt countries voting against it (a case we have no a priori reason to discard), then the use of a 1-tailed test is inappropriate here.
Actually, having read TFA, I'm pretty sure that correlation isn't appropriate at all here. The corruption scores are discrete, categorical values, rather than continuous values. This calls for nonparametric methods. Start with chi-square and move on from there. You can't do correlation with a straight face if your variables are discrete, since there's no guarantee that the "distance" in corruption between 2 and 3 is the same as the distance between 4 and 5.
just because 78% of the 16-18 drink large amount of soda and 93% of the 16-18 year old go to school doesn't mean there is any correlation between the two... That's not a bogus statistics example, that's just an example on how bad people (you in this case) understand what correlation is...
Correlation would be: 85% of the kids 16-18 attending school drink large amounts of soda, whereas only 40% of those who do not attend school drink large amounts of soda. That is an example of correlation.
A good bogus example would be: People who wear suits to work have on average a higher income then people who wear work clothes, there is therefore a correlation between how nicely you dress to go to work and your salary. Therefore the way you dress to work has an impact on your salary.
Please note that the correlation in itself is not the bogus part of the example, the bogus part is the conclusion made by myself. Statistic themselves are rarely bogus, and if they are they can clearly be shown to be bogus, the conclusions drawn are the problematic part.