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.
- Adapted from Taleb's Antifragile: Things That Gain from Disorder
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.
- Adapted from Taleb's Antifragile: Things That Gain from Disorder
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