Talk:Statistical power

Link

The article links to an article which purports to be about using statistical controls to improve power, but the page linked to is not about this, it is about process control, which is a completely different subject.

As for the misleading link, I added a Reliability (psychometric) page for the link to reliability. I don't know that you always have to use psychometrics to increase reliability, although maybe that's just a quibble. Jfitzg

Thank you for fixing the link, but I still think it's not quite right. This is only my opinion, but I think that a user clicking on 'reliability' does not expect to go to an article on reliability in psychometrics. What about in other branches of statistics? We should use the principle of least surprise, and make the link explicit, e.g. "by increasing the reliability of measures, as in the case of psychometric reliability". -- Heron

Good idea. Jfitzg

On the meaning of power

The article says, "The power of the test is the probability that when the test concludes that there a statistically significant difference between test scores for men and women, that the difference found reflects a true difference between the populations of men and women." That seems backwards to me. Rephrasing, it says, "The power of the test is the probability that when the test rejects the null hypothesis, that the null hypothesis is false." Isn't that backwards?

I think the sentence should read, "The power of the test is the probability that when there is a true difference between the test scores of men and women, the test concludes that there a statistically significant difference between the populations of men and women." --Kent37 00:24, 12 October 2006 (UTC)

Power = probability of rejecting a valid null hypothesis??? Wrong! That is the exact opposite of the truth. Power is the probability of rejection, usually as a function of a parameter of interest, and one is interested in having a powerful test in order to be assured of rejection of a false null hypothesis. Michael Hardy 19:52 2 Jun 2003 (UTC)

Increasing the power of a test does not increase the probability of type I error if the increase in power results from an increase in sample size. I have deleted that statement. Michael Hardy 19:58 2 Jun 2003 (UTC)

Thanks for the corrections. Valid was a slip, as the next paragraph shows. Bad place to make a slip, though. I was afraid I had left the impression that increasing power increases the chance of Type I error, and had already made a change to avoid leaving the impression, but apparently it wasn't good enough. Jfitzg

beta

It might be worth mentioning that some texts define beta = power, not 1 - power. See for example Bickel & Doksum 2nd edition page 217. Btyner 19:47, 7 November 2006 (UTC)

anyway a section can added for the stats newbie, using a more intuitive/conceptual approach? i found the posting difficult to follow because i didn't know what half the terms meant. 204.141.184.245 16:14, 20 July 2007 (UTC) N

Removed post-hoc power

I've removed the mention of post-hoc power calculations from the 2nd para as they are generally agreed to be a bad idea, certainly in the form that was stated (power for the sample size you used and the effect you estimated) when the power is a function of the p-value alone. For more details google "post-hoc power" or see this thread on the Medstats discussion list. --Qwfp (talk) 19:03, 23 January 2008 (UTC)

Null Hypothesis

Remember that greater power means higher likelihood of getting a statistically significant result, which could still be the null hypothesis.

Huh? A statistically significant result is a result that rejects the null hypothesis, right? 68.239.78.86 (talk) 04:38, 7 March 2009 (UTC)

The null hypothesis is that there is no effect. Power is the ability to detect an effect if it is there. You do not need power to detect the null hypothesis, because it is assumed a priori that the null is true, until a significant effect is found.

A "statistically significant result" is significant because either 1) a true effect was found, or 2) a type-I error occurred, where it looked like there was a true effect but actually the difference came about by chance.

I'll remove this statement. —Preceding unsigned comment added by 78.32.109.194 (talk) 23:42, 26 May 2009 (UTC)

Sectioning

I just added a provisional structure to the article by introducing some sections. I was surprised to find that an article on such an important statistical concept basically was one long piece of text. The matter at hand is certainly not a simple one and I feel its various aspects merit individual attention. The division into sections may make it slightly more easy to read, and perhaps some readers are interested in only certain sections. It's by no means perfect, and I'll be glad to see others improve it, renaming sections, subdividing (currently, the Background section is somewhat long and diverse, and the Intro a bit technical perhaps?). Cheers! Evlshout (talk) 05:37, 31 March 2009 (UTC)

Calculating statistical power

Power analysis can either be done before (a priori or prospective power analysis) or after (post hoc or retrospective power analysis) data is collected.

There is no information on the wiki about how to calculate the statistical power. Anyway, I can not find it elsewhere either, and had expected the wiki to state this, as such calculations are present in most other statistical wikis. —Preceding unsigned comment added by DBodor (talk • contribs) 17:09, 14 April 2009 (UTC)

What does "trade-off" mean?

The article says "most researchers assess the power of their tests using 0.80 as a standard for adequacy. This convention implies a four-to-one trade off between β-risk and α-risk". Does "trade-off" mean "ratio". How would this be calculated. Power is said to be a function, which is not, in general, a single number, so it depends on the size of the "effect". If thse researchers wish to calculate a difference or ratio of numbers, are they assuming a particular number for the "effect"?

Tashiro (talk) 14:31, 9 September 2011 (UTC)

Composite hypothesis

As the alternative mostly is composie, it is too simple to suggest the power is just one number. Nijdam (talk) 22:48, 18 October 2011 (UTC)

Title

As far as I know the title does not cover the subject. The power of a test isn't called statistical power. My suggestion: change the title to "Power (statistics)". Nijdam (talk) 22:01, 14 October 2011 (UTC)