by From Lars Syll Data without theory can lead to bogus inferences … Before being comforted or alarmed, consider whether it makes sense to extrapolate. Is there a persuasive reason why the future can be predicted simply by looking at the past? Or is that wishful thinking? Or nothing at all? … Remember that even random flips can yield striking, even stunning, patterns that mean nothing at all … A statistical comparison of two things is similarly unpersuasive unless there is a logical reason why they should be related … Ask yourself whether the people who did the study thought before calculating. The central problem with the present ‘machine learning’ and ‘big data’ hype is that so many — falsely — think that they can get away with analysing real-world phenomena without any (commitment to)
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from Lars Syll
Data without theory can lead to bogus inferences …
Before being comforted or alarmed, consider whether it makes sense to extrapolate. Is there a persuasive reason why the future can be predicted simply by looking at the past? Or is that wishful thinking? Or nothing at all? …
Remember that even random flips can yield striking, even stunning, patterns that mean nothing at all …
A statistical comparison of two things is similarly unpersuasive unless there is a logical reason why they should be related … Ask yourself whether the people who did the study thought before calculating.
The central problem with the present ‘machine learning’ and ‘big data’ hype is that so many — falsely — think that they can get away with analysing real-world phenomena without any (commitment to) theory. But — data never speaks for itself. Without a prior statistical set-up, there actually are no data at all to process. And — using a machine learning algorithm will only produce what you are looking for.
Machine learning algorithms always express a view of what constitutes a pattern or regularity. They are never theory-neutral.
Clever data-mining tricks are not enough to answer important scientific questions. Theory matters.