I don’t know how it is elsewhere, but in the US they aren’t “kinda considered” doctors, they are doctors. They have terminal medical degrees and practitioner’s licenses same as any other medical practitioner. They’re kinda segmented off from the rest of medical practice because of how dentistry evolved alongside other historical healing practices, but they are doctors.
Second, is statistics not a branch of mathematics? The courses I took on probability and statistics were taught by the math department. I don’t see how it can’t be. Is it “pure” math? Depends on how you define pure but probably not. Is it “easy” math? Arguably some of it is, though I think people who think stats is an easy science probably aren’t very good at it. All that I get. But the idea that it is (uniquely among technical disciplines) “not math” is… confounding to me.
I think the chasm really is within the statistics discipline, less so between statistics a d mathematics. If you’re doing statistics research you’re essentially a mathematician in the statistics subfield. If you’re applying statistics in another field, you’re a statistician in the professional worker sense.
I think that’s so, but that relationship between theory and application exists for every technical discipline. Computer scientists vs software engineers, for example. The line is usually blurry; I tend to operate closer to the applied side of software, for instance, but I still think about and am informed by the theoretical side, just as theory is shaped by the experimental results of application.
“Mathematics” is an odd case because what people call “pure mathematics” is upstream of even the theoretical side of technical specialties. Like, what a theoretical mathematician might call an applied mathematician, I might call a data scientist, because they’re closer to pure theory than I am, but still closer to technical application than the theoretical mathematician. It’s a super-theory that underpins other theoretical domains.
Statistics results change based on the lens through which you interpret the data. Pure math doesn’t do that. Assigning probabilities is arguably pure math, but assigning error bars is purely subjective. It’s more a reflection of the subjective selection and definition processes than of the underlying probabilities.
I don’t know where you got this idea from but it is incorrect. Error bars are used to indicate uncertainty in measurements and they are used to indicate confidence (or lack thereof) in those measurements. Measurement is hard, and precise measurement is harder, so engineers of all stripes use error bars to indicate how precisely their data have been recorded. It’s not just a stats thing.
also this:
results change based on the lens through which you interpret the data
happens in every field, including pure mathematics. Look up the axiom of choice if you would like a lot of further reading about the implications of interpreting mathematics through that particular lens. Much as we may long for a “purely objective” language of the universe, free from the limitations of human interpretation, we haven’t discovered it yet. The best we can do for now is try to make good assumptions and build from there.
Not even that is pure math. It depends on your prior knowledge, for example if you think one event is more likely. On the other hand if you don’t include prior knowledge/assumptions like one event being more likely you’re implying that the prior knowledge behaves in a way that makes your combination of probabilities and data the way it is (for example a flat prior, aka every event has the same likelihood, but in some cases it gets even weirder and would effectively force an absurd prior, so you typically just avoid that by defining some prior knowledge beforehand).
I don’t know how it is elsewhere, but in the US they aren’t “kinda considered” doctors, they are doctors. They have terminal medical degrees and practitioner’s licenses same as any other medical practitioner. They’re kinda segmented off from the rest of medical practice because of how dentistry evolved alongside other historical healing practices, but they are doctors.
Second, is statistics not a branch of mathematics? The courses I took on probability and statistics were taught by the math department. I don’t see how it can’t be. Is it “pure” math? Depends on how you define pure but probably not. Is it “easy” math? Arguably some of it is, though I think people who think stats is an easy science probably aren’t very good at it. All that I get. But the idea that it is (uniquely among technical disciplines) “not math” is… confounding to me.
I think the chasm really is within the statistics discipline, less so between statistics a d mathematics. If you’re doing statistics research you’re essentially a mathematician in the statistics subfield. If you’re applying statistics in another field, you’re a statistician in the professional worker sense.
I think that’s so, but that relationship between theory and application exists for every technical discipline. Computer scientists vs software engineers, for example. The line is usually blurry; I tend to operate closer to the applied side of software, for instance, but I still think about and am informed by the theoretical side, just as theory is shaped by the experimental results of application.
“Mathematics” is an odd case because what people call “pure mathematics” is upstream of even the theoretical side of technical specialties. Like, what a theoretical mathematician might call an applied mathematician, I might call a data scientist, because they’re closer to pure theory than I am, but still closer to technical application than the theoretical mathematician. It’s a super-theory that underpins other theoretical domains.
Nice summary! And I completely agree, mathematics provide a toolbox for reasoning. A proof about a model is the cheapest way of obtaining knowledge
Statistics results change based on the lens through which you interpret the data. Pure math doesn’t do that. Assigning probabilities is arguably pure math, but assigning error bars is purely subjective. It’s more a reflection of the subjective selection and definition processes than of the underlying probabilities.
I don’t know where you got this idea from but it is incorrect. Error bars are used to indicate uncertainty in measurements and they are used to indicate confidence (or lack thereof) in those measurements. Measurement is hard, and precise measurement is harder, so engineers of all stripes use error bars to indicate how precisely their data have been recorded. It’s not just a stats thing.
also this:
happens in every field, including pure mathematics. Look up the axiom of choice if you would like a lot of further reading about the implications of interpreting mathematics through that particular lens. Much as we may long for a “purely objective” language of the universe, free from the limitations of human interpretation, we haven’t discovered it yet. The best we can do for now is try to make good assumptions and build from there.
Not even that is pure math. It depends on your prior knowledge, for example if you think one event is more likely. On the other hand if you don’t include prior knowledge/assumptions like one event being more likely you’re implying that the prior knowledge behaves in a way that makes your combination of probabilities and data the way it is (for example a flat prior, aka every event has the same likelihood, but in some cases it gets even weirder and would effectively force an absurd prior, so you typically just avoid that by defining some prior knowledge beforehand).