Dselby86
commented
2 years ago Hi Dylan
You can also think of the t test as having a mean of your stay/switch win percent and a sample size of 1000
On Sun, Jan 23, 2022, 9:16 PM dholford @.***> wrote:
For Lab 2 Part 2 we are asked about the "dominant" strategy. In the scenario where there are 5 goats and 2 cars my table looked like this: outcome strategy LOSE WIN stay 0.72 0.28 switch 0.66 0.34
I then starting thinking, well, ultimately I'd want to know if the difference in win % between staying and switching was actually statistically significant. I have to admit, I don't remember exactly how to figure that out. I started by looking for ways to do this in R, and was playing around with t.test() I realized, though, that in running 10,000 games we still ended with 1 mean. To actually run a t.test, we would want a distribution of means. So we would need to run another loop, or connect another loop to the current functions to calculate the mean win % for stay and switch for a given scenario and do that over and over so we ended up with enough means to run a t-test.
I'm I thinking about that correctly?
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dholford
For Lab 2 Part 2 we are asked about the "dominant" strategy. In the scenario where there are 5 goats and 2 cars my table looked like this: outcome strategy LOSE WIN stay 0.72 0.28 switch 0.66 0.34
I then starting thinking, well, ultimately I'd want to know if the difference in win % between staying and switching was actually statistically significant. I have to admit, I don't remember exactly how to figure that out. I started by looking for ways to do this in R, and was playing around with t.test() I realized, though, that in running 10,000 games we still ended with 1 mean. To actually run a t.test, we would want a distribution of means. So we would need to run another loop, or connect another loop to the current functions to calculate the mean win % for stay and switch for a given scenario and do that over and over so we ended up with enough means to run a t-test.
I'm I thinking about that correctly?