4 ms·

I think that as long as one remember that a lot of words used in statistics have very different meanings from how one would use them in normal life one should b

I think that as long as one remember that a lot of words used in statistics have very different meanings from how one would use them in normal life one should be fine. "Controlling for" is one such term, "explains" is another. Of course, especially once study result make it into mass media all statistical meaning of the words used is completely lost and reinterpreted from the point of view of normal-life usage.

viraptor 10y ago
Could you say how "explains" is different in statistics and normal-life? I know what it means in statistics, but they're actually pretty close, so I'm not sure how it could lead to a non-trivial misunderstanding.
- Noseshine 10y ago
  Statistical "explains" is just that. It is based purely on the model and numbers. Try this: Create statistical "explanations" for things like Newton's laws, or for everyday explanations like why the uncle didn't go on vacation this year. When you get a statistical "explanation", for example in a linear model, factor X explains 50% of the observed variation, you never know if that is actually the factor - or if it's random, or if it's confounded by an unknown. "explains" in statistics is meant to be used within the context of your model equations and numbers. Even if you get causation by experimenting and controlling the variables and don't just have correlation, who would be satisfied with a statistical "explanation"? You still won't know if there isn't a common factor that you just don't know about yet. Same with statistical significance: It is useless unless someone who actually knows the field checks how/if the model you have relates to the real world. It's not enough to show that everything works fine within your model - you have to know if you have the right model, which requires knowledge outside of statistics. It's just a tool (statistics), by itself it means as much as naked numbers.
  - Noseshine 10y ago
    ...or in other words (can't edit what I wrote any more): Statistics can tell you that there is a connection. It can't tell you how (it works).
aab0 10y ago
'significant' is another good one.

viraptor 10y ago

Could you say how "explains" is different in statistics and normal-life? I know what it means in statistics, but they're actually pretty close, so I'm not sure how it could lead to a non-trivial misunderstanding.

Noseshine 10y ago
Statistical "explains" is just that. It is based purely on the model and numbers. Try this: Create statistical "explanations" for things like Newton's laws, or for everyday explanations like why the uncle didn't go on vacation this year. When you get a statistical "explanation", for example in a linear model, factor X explains 50% of the observed variation, you never know if that is actually the factor - or if it's random, or if it's confounded by an unknown. "explains" in statistics is meant to be used within the context of your model equations and numbers. Even if you get causation by experimenting and controlling the variables and don't just have correlation, who would be satisfied with a statistical "explanation"? You still won't know if there isn't a common factor that you just don't know about yet. Same with statistical significance: It is useless unless someone who actually knows the field checks how/if the model you have relates to the real world. It's not enough to show that everything works fine within your model - you have to know if you have the right model, which requires knowledge outside of statistics. It's just a tool (statistics), by itself it means as much as naked numbers.
- Noseshine 10y ago
  ...or in other words (can't edit what I wrote any more): Statistics can tell you that there is a connection. It can't tell you how (it works).

aab0 10y ago

'significant' is another good one.