Most people with a strong intuition for statistics have taken courses in probability. It is foundational material for the discipline.
If you haven’t taken a probability course, and if you’re serious about wanting to learn stats well, I would strongly recommend to start there. I think Harvard’s intro probability course is good and has free materials: https://projects.iq.harvard.edu/stat110/youtube
I’ve taught out of Freedman, but not the other texts. It’s well written, but it is targeted at a math-phobic audience. A fine choice if you do not wish to embark on the long path
Statistics is trying to “invert” what probability does.
Probability starts with a model, and then describes what will happen given the model’s assumptions.
Statistics goes the opposite direction: it is about using data to put limits on the set of reasonable/plausible models. The logic is something like: “if the model had property X, then probability theory says I should have seen Y. But, NOT Y. Therefore, NOT X.” It’s invoking probability to get the job done.
Applying statistical techniques without understanding the probability models involved is like having a toolbox, without understanding why any of the tools work.
It all goes fine until the tools fail (which happens often, and often silently) and then you’re hosed. You may fail to notice the problems entirely, or may have to outsource judgments to others with more experience.
Being able to accurately assess a paper’s claims is, unfortunately, a very high bar. A large proportion of scientists fall short of it. see: [https://statmodeling.stat.columbia.edu/2022/03/05/statistics-is-hard-etc-again/]
Most people with a strong intuition for statistics have taken courses in probability. It is foundational material for the discipline.
If you haven’t taken a probability course, and if you’re serious about wanting to learn stats well, I would strongly recommend to start there. I think Harvard’s intro probability course is good and has free materials: https://projects.iq.harvard.edu/stat110/youtube
I’ve taught out of Freedman, but not the other texts. It’s well written, but it is targeted at a math-phobic audience. A fine choice if you do not wish to embark on the long path
Thanks! I’ll look this over.
Out of curiosity,
Do some people learn statistics without learning probability? Or, what’s different for someone who learns only stats and not probability?
(I’m trying to grasp what shape/boundaries are at play between these two bodies of knowledge)
Statistics is trying to “invert” what probability does.
Probability starts with a model, and then describes what will happen given the model’s assumptions.
Statistics goes the opposite direction: it is about using data to put limits on the set of reasonable/plausible models. The logic is something like: “if the model had property X, then probability theory says I should have seen Y. But, NOT Y. Therefore, NOT X.” It’s invoking probability to get the job done.
Applying statistical techniques without understanding the probability models involved is like having a toolbox, without understanding why any of the tools work.
It all goes fine until the tools fail (which happens often, and often silently) and then you’re hosed. You may fail to notice the problems entirely, or may have to outsource judgments to others with more experience.
Thanks, this is incredibly useful.
I think I understand enough to put together a curriculum to delve into this topic. Starting with the harvard course you recommended.