[HN Gopher] Statisticians use a technique that leverages randomn...
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Statisticians use a technique that leverages randomness to deal
with the unknown
Author : Duximo
Score : 34 points
Date : 2024-10-03 07:08 UTC (2 days ago)
(HTM) web link (www.quantamagazine.org)
(TXT) w3m dump (www.quantamagazine.org)
| xiaodai wrote:
| I don't know. I find quanta articles very high noise. It's always
| hyping something
| jll29 wrote:
| I don't find the language of the article full of "hype"; they
| describe the history of different forms of imputation from
| single to multiple to ML-based.
|
| The table is particularly useful as it describes what the
| article is all about in a way that can stick to students'
| minds. I'm very grateful for QuantaMagazine for its popular
| science reporting.
| vouaobrasil wrote:
| I agree with that. I skip the Quanta magazine articles, mainly
| because the titles seem to be a little to hyped for my taste
| and don't represent the content as well as they should.
| light_hue_1 wrote:
| I wish they actually engaged with this issue instead of writing a
| fluff piece. There are plenty of problems with multiple
| imputation.
|
| Not the least of which is that it's far too easy to do the
| equivalent of p hacking and get your data to be significant by
| playing games with how you do the imputation. Garbage in, garbage
| out.
|
| I think all of these methods should be abolished from the
| curriculum entirely. When I review papers in the ML/AI I
| automatically reject any paper or dataset that uses imputation.
|
| This is all a consequence of the terrible statics used in most
| fields. Bayesian methods don't need to do this.
| jll29 wrote:
| There are plenty of legit. articles that discuss/survey
| imputation in ML/AI:
| https://scholar.google.com/scholar?hl=de&as_sdt=0%2C5&q=%22m...
| light_hue_1 wrote:
| The prestigious journal "Artificial intelligence in
| medicine"? No. Just because it's on Google scholar doesn't
| mean it's worth anything. These are almost all trash. On the
| first page there's one maybe legit paper in an ok venue as
| far as ML is concerned (KDD; an adjacent field to ML) that's
| 30 years old.
|
| No. AI/ML folks don't do imputation on our datasets. I cannot
| think of a single major dataset in vision, nlp, or robotics
| that does so. Despite missing data being a huge issue in
| those fields. It's an antiqued method for an antiqued idea of
| how statistics should work that is doing far more damage than
| good.
| DAGdug wrote:
| Maybe in academia, where sketchy incentives rule. In industry,
| p-hacking is great till you're eventually caught for doing
| nonsense that isn't driving real impact (still, the lead time
| is enough to mint money).
| light_hue_1 wrote:
| Very doubtful. There are plenty of drugs that get approved
| and are of questionable value. Plenty of procedures that turn
| out to be not useful. The incentives in industry are even
| worse because everything depends on lying with data if you
| can do it.
| hggigg wrote:
| Indeed. Even worse some entire academic fields are built on
| pillars of lies. I was married to a researcher in one of
| them. Anything that compromises the existence of the field
| just gets written off. The end game is this fed into life
| changing healthcare decisions so one should never assume
| academia is harmless. This was utterly painful watching it
| from the perspective of a mathematician.
| clircle wrote:
| Does any living statistician come close to the level of Donald
| Rubin in terms of research impact? Missing data analysis, causal
| inference, EM algorithm, any probably more. He just walks around
| creating new subfields.
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