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Jan 11, 2022, 1:22:04 PMJan 11

to

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?

Thanks,

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?

Thanks,

Jan 11, 2022, 2:34:31 PMJan 11

to

On Tue, 11 Jan 2022 10:22:02 -0800 (PST), Cosine <ase...@gmail.com>

wrote:

>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?

>

Cost. And your friends may make fun of you if you spend too much.

I want a car that has great acceleration and top speed and outstanding

fuel economical. It should also be roomy and fun to drive. It should

look good. Especiallly, it should be cheap to buy and to insure.

Unfortunate, that dream. Buying, I've always settled for a limited

choice among "what's available" and what is convenient.

--

Rich Ulrich

wrote:

>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?

>

I want a car that has great acceleration and top speed and outstanding

fuel economical. It should also be roomy and fun to drive. It should

look good. Especiallly, it should be cheap to buy and to insure.

Unfortunate, that dream. Buying, I've always settled for a limited

choice among "what's available" and what is convenient.

--

Rich Ulrich

Jan 11, 2022, 10:10:25 PMJan 11

to

Cosine 在 2022年1月12日 星期三上午2:22:04 [UTC+8] 的信中寫道：

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)?

> 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:
>

> 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?

>

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)?

Jan 13, 2022, 8:24:29 PMJan 13

to

On Tue, 11 Jan 2022 19:10:23 -0800 (PST), Cosine <ase...@gmail.com>

wrote:

>Cosine ? 2022?1?12? ?????2:22:04 [UTC+8] ??????

from ESTIMATION. Testing starts by designating a cutoff.

Estimation reports "effect sizes" -- usually, in natural units of the

experiment, rather than by comparing p-values.

Yeah, I know that if tests are entirely commensurate (same Ns,

SDs,), then I know that a p= 0.001 reflects a t-test mean-difference

which is about twice that for p= 0.05. I would only ever mention

the comparisosn if I were already engaged in explaining "effect

sizes" in a more thorough way, such as "Why we should ignore

all the 'tiny' effects where p is not < 0.001."

And this statement of yours is not the way statisticians ever discuss

either -- "For p-value, suppose we have ....".

I assume that you intended to say something like, "For two new

diagnostic methods, we have tests (with p-values) comparing each

to a standard and also to each other."

In a testing environment, or when one is TALKing about testing,

we never would ASSERT that A is better than B unless the test

for A vs B has a p-value meets the cutoff that was designated.

>

> 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)?

--

Rich Ulrich

wrote:

>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?

I think you want to mull the idea that TESTing is separate
>>

>> 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?

from ESTIMATION. Testing starts by designating a cutoff.

Estimation reports "effect sizes" -- usually, in natural units of the

experiment, rather than by comparing p-values.

Yeah, I know that if tests are entirely commensurate (same Ns,

SDs,), then I know that a p= 0.001 reflects a t-test mean-difference

which is about twice that for p= 0.05. I would only ever mention

the comparisosn if I were already engaged in explaining "effect

sizes" in a more thorough way, such as "Why we should ignore

all the 'tiny' effects where p is not < 0.001."

And this statement of yours is not the way statisticians ever discuss

either -- "For p-value, suppose we have ....".

I assume that you intended to say something like, "For two new

diagnostic methods, we have tests (with p-values) comparing each

to a standard and also to each other."

In a testing environment, or when one is TALKing about testing,

we never would ASSERT that A is better than B unless the test

for A vs B has a p-value meets the cutoff that was designated.

>

> 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)?

Rich Ulrich

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