“We’re more fooled by noise than ever before, and it’s because of a nasty phenomenon called “big data.” With big data, researchers have brought cherry-picking to an industrial level.
Modernity provides too many variables, but too little data per variable. So the spurious relationships grow much, much faster than real information.
In other words: Big data may mean more information, but it also means more false information.
Just like bankers who own a free option — where they make the profits and transfer losses to others – researchers have the ability to pick whatever statistics confirm their beliefs (or show good results) … and then ditch the rest.
Big-data researchers have the option to stop doing their research once they have the right result. In options language: The researcher gets the “upside” and truth gets the “downside.” It makes him antifragile, that is, capable of benefiting from complexity and uncertainty — and at the expense of others.
But beyond that, big data means anyone can find fake statistical relationships, since the spurious rises to the surface. This is because in large data sets, large deviations are vastly more attributable to variance (or noise) than to information (or signal). It’s a property of sampling: In real life there is no cherry-picking, but on the researcher’s computer, there is. Large deviations are likely to be bogus.
We used to have protections in place for this kind of thing, but big data makes spurious claims even more tempting. And fewer and fewer papers today have results that replicate: Not only is it hard to get funding for repeat studies, but this kind of research doesn’t make anyone a hero. Despite claims to advance knowledge, you can hardly trust statistically oriented sciences or empirical studies these days.
This is not all bad news though: If such studies cannot be used to confirm, they can be effectively used to debunk — to tell us what’s wrong with a theory, not whether a theory is right.
Another issue with big data is the distinction between real life and libraries. Because of excess data as compared to real signals, someone looking at history from the vantage point of a library will necessarily find many more spurious relationships than one who sees matters in the making; he will be duped by more epiphenomena. Even experiments can be marred with bias, especially when researchers hide failed attempts or formulate a hypothesis after the results — thus fitting the hypothesis to the experiment (though the bias is smaller there).
This is the tragedy of big data: The more variables, the more correlations that can show significance. Falsity also grows faster than information; it is nonlinear (convex) with respect to data (this convexity in fact resembles that of a financial option payoff). Noise is antifragile. Source: N.N. Taleb
The problem with big data, in fact, is not unlike the problem with observational studies in medical research. In observational studies, statistical relationships are examined on the researcher’s computer. In double-blind cohort experiments, however, information is extracted in a way that mimics real life. The former produces all manner of results that tend to be spurious (as last computed by John Ioannidis) more than eight times out of 10….”