Talk:Power of a test

message: 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)
thread_title:  beta 

message: 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)
thread_title:  Removed post-hoc power 

message: 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)
thread_title:  Null Hypothesis 

message: 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)
thread_title:  Sectioning 

message: 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)
thread_title:  Calculating statistical power 

message: 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)
thread_title: What does "trade-off" mean?

message: 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)
thread_title:  Composite hypothesis 

message: 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)
thread_title:  Title 

message: As odd as it may sound to those who love editing pages on topics they know nothing about, there are those of us who actually have to implement some of the methodologies presented on some of these wonderful pages.  I make power and sample size calculations for real life work.  Not everyday, but from time to time.  I refer myself back to wikipedia to find links to all sorts of software on several methods pages.  When I came back to this page to recall a link to a FREE resource I'd put to good use in the past had been removed, I was extremely disappointed.  I literally waded through the mass of edits to find it.  I hope the next person doesn't have to do the same.  If you don't want links to for-pay software -- fine.  But PLEASE leave the free ones, especially if they're open source for crying out loud.  — Preceding unsigned comment added by TedPSS (talk • contribs) 05:14, 7 July 2016 (UTC)
replies: text: - I agree with you that such links are important, but I think the issue for some editors is avoiding the External references section becoming a list that dwarfs the article as people add more and more links to software (such as detailed in WP:NOTLINK). Are there any lists of free and/or open-source software that can be linked in the external references? This may be a tidier solution. — Sasuke Sarutobi (talk) 07:46, 14 October 2016 (UTC)
replies: text: The UCSF Biostatistics department has a list of power and sample size programs and websites and allows one to view by cost if so wished. It was last updated in 2006, however. Qwfp (talk) 12:31, 14 October 2016 (UTC)
thread_title:  Please stop removing links to free software 

message: The intro deems power to be the sensitivity, but notes that power is 1 - (the false negative rate) = the true negative rate. That sounds like **specificity** to me.  — Preceding unsigned comment added by Zeng.chi (talk • contribs) 08:50, 10 August 2016 (UTC)
replies: text: No, 1 – (false negative rate) = (true positive rate), as per the article Sensitivity and specificity#Definitions. Power = 1 – (false negative rate) = 1-\tfrac{FN}{TP+FN}=\tfrac{TP}{TP+FN} = sensitivity. Loraof (talk) 21:28, 19 September 2017 (UTC)
thread_title:  Isn't power specificity, not sensitivity? 

message: "the probability that the test correctly rejects the null hypothesis (H0) when the alternative hypothesis (H1) is true"
Non sequitur. Rejection of null hypothesis doesn't imply that the alternative one is true.  — Preceding unsigned comment added by Pn4vin (talk • contribs) 19:23, 26 March 2017 (UTC)
replies: text: It doesn't say that rejection of the null hypothesis implies the alternative one is true—it says the reverse of that: If the alternative one is true, it's correct to reject the null. Loraof (talk) 20:24, 19 September 2017 (UTC)
thread_title:  Misleading jump in reasoning 

message: Am I just delusional or is the definition given here for statistical power exactly the formula used to define Recall in Precision_and_recall, i.e. true positives divided by the sum of true positives and false negatives (cf. Type_I_and_type_II_errors#Table_of_error_types for clarification)? If so, why is there no link to Precision_and_recall here and no link to this article in Precision_and_recall? Catskineater (talk) 01:24, 5 December 2017 (UTC)
thread_title:  Link to Recall (as in Precision and Recall) ? 

message: The example uses \mu_D, \bar{D}_n,\ and\ \theta   for the same variable.  This is confusing.  — Preceding unsigned comment added by Tedtoal (talk • contribs) 22:02, 2 March 2020 (UTC)
thread_title:  Misleading variables in example ? 

message: The article says that increasing α would increase statistical power, because you set a weaker threshold to reject the null, and therefore you'll end up with statistically significant results more often. I don't understand how this connects with statistical power though. If we talk about the conditional probability of getting a stat significant result given that the null is true, then increasing α increases the likelihood of a positive (and inaccurate) result. On the other hand, if we look at the conditional probability that a result is accurate given that it is stat significant, then decreasing α would increase this probability. I don't see how, however, α would have impacts on statistical power, which is a conditional probability given that the null is false, i.e., stat power is sort of like sensitivity, or how often will I get a stat significant result when there is an actual effect present. α is conditional on the effect not being there, so I don't quite understand why changes to the conditional probability α given that the null is true affect the conditional probability of stat power given that the null is false. It's like it's two different realms, α is related to the specificity of the experiment and β is related to its sensitivity, and these two are independent events so I don't see how they affect one another.
In other words, sensitivity (1-β or stat power) and 1-sensitivity (type II error) always add up to 1. Specificity (1-α) and non-specificity (type I error) also always add up to 1. But the sum of sensitivity and specificity are not constrained by a similar relationship, hence α and β are also not so related as the text suggests Themidget17 (talk) 20:55, 5 May 2021 (UTC)
thread_title:  Effect of α on statistical power? 

message: https://en.wikipedia.org/wiki/File:Statistical_test,_significance_level,_power.png
looking at other examples on the internet, the type 1 error rate is in a different spot for two-sample tests
https://liwaiwai.com/wp-content/uploads/2019/10/stat-type1-type2-error-1.png
https://microbenotes.com/wp-content/uploads/2020/07/Type-I-Error-and-Type-II-Error-with-10-Differences.jpg
https://miro.medium.com/v2/resize:fit:1400/1*qXmpMtzvQM1z8ov1DUwbew.png
https://www.mdpi.com/make/make-01-00054/article_deploy/html/images/make-01-00054-g005.png 71.40.21.124 (talk) 02:29, 12 February 2024 (UTC)
thread_title:  is the image wrong?