That’s not very much at all: more than 98% of the reasons co-operation varies in the dataset are nothing to do with what year it is! But it’s still completely fine to say that there’s a correlation, because correlations can be any size, and not every effect that’s of interest is a strong effect. 13 to use the standard phrasing, the year “explains 1.69% of the variance” in co-operation (that would be the R-squared ). If you look at the paper, you find that it’s a correlation of r =. It’s true that the correlation in the co-operation graph is weak. Which leads us to…Īrgument 2: There’s only a very weak correlation between the variables. On that basis, it’s absolutely fine to draw the interpretation “there is a correlation between co-operation and year”, as long as you also note that the correlation is weak: the reason it looks so messy and noisy, and the reason the correlation is hard to discern with the naked eye, is that it’s a really small correlation. It’s simply the case that if you run a regression or correlation analysis on the “co-operation” and “year” variables from the study above, the statistics will tell you that there’s a correlation, and in this case that correlation is statistically significant. That makes it harder to just “eyeball” a graph that pops up on Twitter and decide you know what the correlation is - but again, that’s why we have statistics. That’s because in the real world there’s often a lot of statistical noise, and a lot of variation. But in real life, datasets often look like the first graph above.
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