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That's the difference between inference and prediction. Both statistics and machine learning can do either of those things.

chubot 8y ago
Can you give some examples of both?
- Spivak 8y ago
  Statistics being used for prediction: https://fivethirtyeight.com/features/how-our-march-madness-predictions-work/ https://fivethirtyeight.com/features/how-our-march-madness-p... Statistics being used for inference: https://fivethirtyeight.com/features/stephen-curry-is-the-revolution/ https://fivethirtyeight.com/features/stephen-curry-is-the-re...
  - lottin 8y ago
    As others have pointed out, in statistics the goal is understanding something. Once you have understood it you can predict its behaviour. However the reverse is not true.
    - thousandautumns 8y ago
      This isn't true though. There are ton's of uninterpretable model methodologies in classical statistics that have little to no ability to allow for understanding but are aimed entirely at accurate predictions. Where is this narrative that statistics is only interested in understanding coming from?
      - srean 8y ago
        > Where is this narrative that statistics is only interested in understanding coming from? You may have heard of this guy called Fisher he might like to have a few words with you. He says he fathered modern Stats, that he connected what was a bag of recipes to math. Many seem to agree [0] despite the fact he does not seem to be the most pleasant bloke around. The British queen seemed awfully impressed with him though, YMMV. [0] "a genius who almost single handedly created the foundations for modern statistical science" -- Hald, Anders, A History of Mathematical Statistics.
        thousandautumns 8y ago
        I'm sure you were attempting to make a point, but you failed. But if you are trying to claim that R.A. Fisher only cared claims that statistics is only about inference, the point is moot because: 1. R.A. Fisher isn't the Almighty Statistical God just because he did laid a lot of foundations in early statistics. 2. Fisher has a long history of seriously stupid personal-beliefs including but not limited to: refuting anything and everything relating to Bayesian statistics, attempting to discredit the studies done showing a link between smoking and lung cancer, and advocating for eugenics and the idea of superiority/inferiority between races. So just because Fisher may have claimed something doesn't make it so.
srean 8y ago
There is a difference between 'can' and "is the central question". As I said in my comment [0], traditionally statistics is about making claims about the nature of the population from a sample. If one is operating with a parametric model, the running assumption is that one knows absolutely everything about the population except for a few parameters. Those are estimated from the data. One you have done that, one can indeed use those to do prediction and if you were lucky that the data did come from the distribution assumed, then you wont do too badly, otherwise you could perform very poorly. ML on the other hand short circuits the step of making any claims about, or even trying to understand the population. ML straight away jumps to claims about prediction quality. Note there is non-parametric statistics, but even there (barring edge cases) the central object of inquiry is the population. There is also prequential statistics that also jumps directly to claims about prediction without making any claims about understanding the population. Prequential stats is fascinating, and it is what is closes to ML in spirit. [0] https://news.ycombinator.com/item?id=18591143 https://news.ycombinator.com/item?id=18591143

chubot 8y ago

Can you give some examples of both?

Spivak 8y ago
Statistics being used for prediction: https://fivethirtyeight.com/features/how-our-march-madness-predictions-work/ https://fivethirtyeight.com/features/how-our-march-madness-p... Statistics being used for inference: https://fivethirtyeight.com/features/stephen-curry-is-the-revolution/ https://fivethirtyeight.com/features/stephen-curry-is-the-re...
- lottin 8y ago
  As others have pointed out, in statistics the goal is understanding something. Once you have understood it you can predict its behaviour. However the reverse is not true.
  - thousandautumns 8y ago
    This isn't true though. There are ton's of uninterpretable model methodologies in classical statistics that have little to no ability to allow for understanding but are aimed entirely at accurate predictions. Where is this narrative that statistics is only interested in understanding coming from?
    - srean 8y ago
      > Where is this narrative that statistics is only interested in understanding coming from? You may have heard of this guy called Fisher he might like to have a few words with you. He says he fathered modern Stats, that he connected what was a bag of recipes to math. Many seem to agree [0] despite the fact he does not seem to be the most pleasant bloke around. The British queen seemed awfully impressed with him though, YMMV. [0] "a genius who almost single handedly created the foundations for modern statistical science" -- Hald, Anders, A History of Mathematical Statistics.
      - thousandautumns 8y ago
        I'm sure you were attempting to make a point, but you failed. But if you are trying to claim that R.A. Fisher only cared claims that statistics is only about inference, the point is moot because: 1. R.A. Fisher isn't the Almighty Statistical God just because he did laid a lot of foundations in early statistics. 2. Fisher has a long history of seriously stupid personal-beliefs including but not limited to: refuting anything and everything relating to Bayesian statistics, attempting to discredit the studies done showing a link between smoking and lung cancer, and advocating for eugenics and the idea of superiority/inferiority between races. So just because Fisher may have claimed something doesn't make it so.

srean 8y ago

There is a difference between 'can' and "is the central question". As I said in my comment [0], traditionally statistics is about making claims about the nature of the population from a sample. If one is operating with a parametric model, the running assumption is that one knows absolutely everything about the population except for a few parameters. Those are estimated from the data. One you have done that, one can indeed use those to do prediction and if you were lucky that the data did come from the distribution assumed, then you wont do too badly, otherwise you could perform very poorly. ML on the other hand short circuits the step of making any claims about, or even trying to understand the population. ML straight away jumps to claims about prediction quality. Note there is non-parametric statistics, but even there (barring edge cases) the central object of inquiry is the population. There is also prequential statistics that also jumps directly to claims about prediction without making any claims about understanding the population. Prequential stats is fascinating, and it is what is closes to ML in spirit. [0] https://news.ycombinator.com/item?id=18591143 https://news.ycombinator.com/item?id=18591143