Editing 2560: Confounding Variables
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==Explanation== | ==Explanation== | ||
| β | + | {{incomplete|Created by an ULTRASKEPTIC. What if even the data is fake man? We could be in the Matrix dude except unfalsifiable. - Please change this comment when editing this page. Do NOT delete this tag too soon.}} | |
[[Miss Lenhart]] is teaching a course which apparently covers at least an overview of statistics. | [[Miss Lenhart]] is teaching a course which apparently covers at least an overview of statistics. | ||
In statistics, a ''confounding variable'' is a third variable that's related to the independent variable, and also causally related to the dependent variable. An example is that you see a correlation between sunburn rates and ice cream consumption; the confounding variable is temperature: high temperatures cause people go out in the sun and get burned more, and also eat more ice cream. | In statistics, a ''confounding variable'' is a third variable that's related to the independent variable, and also causally related to the dependent variable. An example is that you see a correlation between sunburn rates and ice cream consumption; the confounding variable is temperature: high temperatures cause people go out in the sun and get burned more, and also eat more ice cream. | ||
| β | One way to control for a confounding variable by restricting your data-set to samples with the same value of the confounding variable. But if you do this too much, your choice of that "same value" can produce results that don't generalize. Common examples of this in medical testing are using subjects of the same sex | + | One way to control for a confounding variable by restricting your data-set to samples with the same value of the confounding variable. But if you do this too much, your choice of that "same value" can produce results that don't generalize. Common examples of this in medical testing are using subjects of the same sex -- the results may only be valid for that sex, not for all subjects. |
There can also often be multiple confounding variables. It may be difficult to control for all of them without narrowing down your data-set so much that it's not useful. So you have to choose which variables to control for, and this choice biases your results. | There can also often be multiple confounding variables. It may be difficult to control for all of them without narrowing down your data-set so much that it's not useful. So you have to choose which variables to control for, and this choice biases your results. | ||
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==Transcript== | ==Transcript== | ||
| β | :[Miss Lenhart is holding a pointer and pointing at a board with a large heading with some unreadable text beneath it. Below this there are two graphs with scattered points. In the top graph the points are almost on a straight increasing line. In the bottom the data points seem to be more random. Mrs Lenhart covers most of the right side of the board, but there is more unreadable text to the right of her.] | + | :[Miss Lenhart is holding a pointer and pointing at a board with the a large heading with some unreadable text beneath it. Below this there are two graphs with scattered points. In the top graph the points are almost on a straight increasing line. In the bottom the data points seem to be more random. Mrs Lenhart covers most of the right side of the board, but there is more unreadable text to the right of her.] |
:Miss Lenhart: If you don't control for confounding variables, they'll mask the real effect and mislead you. | :Miss Lenhart: If you don't control for confounding variables, they'll mask the real effect and mislead you. | ||
:Heading: Statistics | :Heading: Statistics | ||
