> Then doesn't alpha, or type I error become an impossibility (i.e, reject a

> true null when the p-value suggests the null is not true).

Alpha tells you the probability that the difference that you are seeing is

due to "pure luck". It has become standard to accept but if we replicate the experiment 100 times wouldn't the same situation

be

> present? >

No is not the same situation, for example if you want to determine if a coin

is loaded you would have to do the experiment (throwing both the coin in

question and the "control") enough times so that you could observe a

statistically significant difference. By throwing them only once you can see

how you could establish a difference with a high chance of error due to

chance alone (hence high p value).

I hope I understood that last one correctly,

Mauricio

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----- Original Message -----

From: "Bryan Kirking"

To:

Sent: Wednesday, January 26, 2005 12:43 PM

Subject: Re: [BIOMCH-L] Stats Power. Report Confidence Limits - p values

> Recognizing that p-value interpretation is a topic that is hotly debated

> and further confused by subtle differences in terminology, I'd like to

pose

> the question:

>

> If "alpha, or type I error" is defined (as best as I know) as the

> probability of rejecting the null hypothesis when the null hypothesis is

true,

>

> And based on the associated p-value or confidence interval, one rejects

the

> null hypothesis, making the null untrue(?)

>

> Then doesn't alpha, or type I error become an impossibility (i.e, reject a

> true null when the p-value suggests the null is not true). I suspect the

> answer comes down to Dr. Greiner's remark that this is only one

experiment,

> but if we replicate the experiment 100 times wouldn't the same situation

be

> present? Does type II error (probability of not rejecting a false null)

> now become the best measure of confidence (and I use confidence for lack

of

> a better term)?

>

> As to predefining the alpha level, the issue becomes even more difficult

to

> me when I consider that most studies I read or perform usually have

> multiple comparisons. If one doesn't set overall confidence levels and

> therefore individual levels a priori, how do we guarantee that the overall

> confidence is maintained? Do we do analyses that "accepts" = 0.026 given

> that another variable is p=0.024 and therefore maintains 0.05? To me,

this

> is a very good reason for keeping with predefined values, as long as those

> values are suitable for your application.

>

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> true null when the p-value suggests the null is not true).

Alpha tells you the probability that the difference that you are seeing is

due to "pure luck". It has become standard to accept but if we replicate the experiment 100 times wouldn't the same situation

be

> present? >

No is not the same situation, for example if you want to determine if a coin

is loaded you would have to do the experiment (throwing both the coin in

question and the "control") enough times so that you could observe a

statistically significant difference. By throwing them only once you can see

how you could establish a difference with a high chance of error due to

chance alone (hence high p value).

I hope I understood that last one correctly,

Mauricio

> -----------------------------------------------------------------

> To unsubscribe send SIGNOFF BIOMCH-L to LISTSERV@nic.surfnet.nl

> For information and archives: http://isb.ri.ccf.org/biomch-l

> -----------------------------------------------------------------

----- Original Message -----

From: "Bryan Kirking"

To:

Sent: Wednesday, January 26, 2005 12:43 PM

Subject: Re: [BIOMCH-L] Stats Power. Report Confidence Limits - p values

> Recognizing that p-value interpretation is a topic that is hotly debated

> and further confused by subtle differences in terminology, I'd like to

pose

> the question:

>

> If "alpha, or type I error" is defined (as best as I know) as the

> probability of rejecting the null hypothesis when the null hypothesis is

true,

>

> And based on the associated p-value or confidence interval, one rejects

the

> null hypothesis, making the null untrue(?)

>

> Then doesn't alpha, or type I error become an impossibility (i.e, reject a

> true null when the p-value suggests the null is not true). I suspect the

> answer comes down to Dr. Greiner's remark that this is only one

experiment,

> but if we replicate the experiment 100 times wouldn't the same situation

be

> present? Does type II error (probability of not rejecting a false null)

> now become the best measure of confidence (and I use confidence for lack

of

> a better term)?

>

> As to predefining the alpha level, the issue becomes even more difficult

to

> me when I consider that most studies I read or perform usually have

> multiple comparisons. If one doesn't set overall confidence levels and

> therefore individual levels a priori, how do we guarantee that the overall

> confidence is maintained? Do we do analyses that "accepts" = 0.026 given

> that another variable is p=0.024 and therefore maintains 0.05? To me,

this

> is a very good reason for keeping with predefined values, as long as those

> values are suitable for your application.

>

> -----------------------------------------------------------------

> To unsubscribe send SIGNOFF BIOMCH-L to LISTSERV@nic.surfnet.nl

> For information and archives: http://isb.ri.ccf.org/biomch-l

> -----------------------------------------------------------------

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