Hi:
When doing a statistical test, we often compute the p-value and confidence interval (CI) at a given significance level of alpha.
Questions arise: would it be better to have a lower p-value? Likewise, would it be better to have a narrower CI? Why and why not?
Hi:
When doing a statistical test, we often compute the p-value and confidence interval (CI) at a given significance level of alpha.
Questions arise: would it be better to have a lower p-value? Likewise, would it be better to have a narrower CI? Why and why not?
Cosine ? 2022?1?12? ?????2:22:04 [UTC+8] ??????
Hi:
When doing a statistical test, we often compute the p-value and confidence interval (CI) at a given significance level of alpha.
Questions arise: would it be better to have a lower p-value? Likewise, would it be better to have a narrower CI? Why and why not?
For p-value, suppose we have two new diagnostic methods, A and B. We want to know:
1) are they both better than the standard method?
2) is method A better than B?
We desing studies and use the accuracy (Acc) to check the performances.
By comparing the methods A and the standard one, we have: Acc_Asmp, p-value_A, CI_A
B Acc_Bsmp, p-value_B, CI_B
If p-value_A < p-value_B, could we say that Acc_Asmp is more significant than Acc_Bsmp?
Similarly, we define the width of CI as WCI. We have WCI_A and WCI_B.
If WCI_A < WCI_B, could we say that Acc_A is more significant or more reliable, since
we could be sure that the true value of Acc_A would fall in a narrower CI (smaller width)?
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