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Suppose I have a proportion estimate defined as the proportion of times some event occurs out of $N$ trials. I call this proportion the sample proportion $\hat{p}$, while I call a second as the true population proportion $p$. Suppose I have something akin to a bootstrap over different inputs, where I can generate many such pairs, and I would like to compare these two values as my evaluation criteria. Generally, I am thinking of just taking the difference as a metric in comparing how different they are:

$$ \hat{p}-p $$

However, are there better metrics for comparing these? Would the ratio be better in certain cases?

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  • $\begingroup$ +1 for the interesting q, but how do you know the population value $p_2?$ $\text{//}$ It would be more common to refer to your $p_2$ parameter as $p$ and your $p_1$ estimate of $p_2$ as $\hat p$. $\endgroup$
    – Dave
    Commented Aug 27, 2022 at 11:10
  • $\begingroup$ @Dave Thanks, I have changed it. I am assuming that the population value is known. For example, I have the actual value and I want to evaluate how well a model gets $\hat{p}$ to the true value $p$. $\endgroup$
    – user321627
    Commented Aug 27, 2022 at 11:52
  • $\begingroup$ If both $p$ and $\hat{p}$ would tend to be small, the difference would also be small, so the ratio would be better, I think. $\endgroup$ Commented Jul 20, 2023 at 21:27
  • $\begingroup$ Possibly related $\endgroup$
    – Dave
    Commented Jul 20, 2023 at 21:35

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