Does it though? medicine is learning that when you do data mining the bar for statistical significance needs to be much higher. When you do the traditional hypothesis/experiment loop and a result hits 95% likely that is good enough. However when you data mine from millions of data points anything interesting is much more likely to be coincidence. Modern statistics is trying to figure out how to handle this.
Data mining is good for generating hypothesis that you can then test with a controlled experiment. It can be used in meta-analysis to find small effects that were not significant in individual experiments but when you combine them they are significant. However it is dangerous to use it alone.
In more depth, if you look at 100 data points and find 5 things that meet the 95% bar - that is 5 out of 100, odds are that all of them are false positives since you studied 100 things and found 5. (this is a gross simplification of things like p-values, real statisticians will cry about it, but the average person has a chance of understanding)
