Talk:P-value
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Does neone actually know how to figure out P or is it all made up?
- Certainly all the textbooks explain how to calculate it in various settings. This article, as now written, implies the answer but is not very explicit. Certainly more could be added to it. Michael Hardy 20:23, 5 May 2005 (UTC)
ahhh i see thank you i still havent managed to find out how to work out the p value for a correlational study using pearsons parametric test......guess i must be looking in the wrong text books!
I have difficluties to understand this article as a lay. May be an example would be good ...
Frequent misunderstandings, part b in commnet: there is a numerical mistake. %29 should be %5.
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I was adding a numerical example to the p-value article, as requested above, but it's all been deleted. I've no idea why. --Robma 00:17, 11 December 2005 (UTC)
Michael Hardy who modified previous statement " If the p-value is 0.1, you have a 10% chance of being wrong if you reject the null hypothesis " should explain this.
- That statement is clearly not true. Michael Hardy 20:15, 28 August 2006 (UTC)
[edit] Transferred comments of User:Xiaowei JIANG
If the p-value is 0.1, you have a 10% chance to reject the null hypothesis if it is true.("Current statatment is confusing", Michael Hardy who modified previous statement " If the p-value is 0.1, you have a 10% chance of being wrong if you reject the null hypothesis " should explain this.). Note that, in the Bayesian context, the P-value has a quite different meaning than in the frequentist context!
Um, I can't make any sense of this page. Can we have a rewrite? -- Arthur ~I agree that this article isn't as clear as it could be - or needs to be (and, as a contributor to it, I take some responsibility for that). The intro, at the very least, needs redoing. Now back to the day-job....Robma 12:27, 5 June 2006 (UTC)
I agree that we should rewrite this page, which may include how to calculate different P-values in various conditions. A good start might come from the calculating of the p-values from randomized experiments.--Xiaowei JIANG 00:19, 20 October 2006 (UTC)
[edit] Shouldn't the P value in the coin example be .115?
Since the null hypothesis is that the coin is not biased at all, shouldn't the universe of events that are as or less favorable to the hypothesis include too many heads AND too many tails? For example, the coin coming up all tails would be more unfavorable to the "fair coin" hypothesis than 14 heads. If the null hyposthesis was "the coin is not biased towards heads (but it may be towards tails)", .058 would be correct.
- But as given, the null hypothesis is not that the coin is fair, but rather that the coin is not unfairly biased toward "heads". If the coin is unfairly biased toward "tails", then the null hypothesis is true. Michael Hardy 20:13, 28 August 2006 (UTC)
[edit] Show some calculations
It would be nice to see some equations or calculations made in the article, that way people are not left standing in the dark wondering where the numbers came from. I know I could follow the article and the example because I have a little experience with probability and statistics. If someone would like, I can post the equations and some other information I feel would be useful.
- Yes, this definitely needs some f-ing formulas. I know what p-values are, but came here in the hopes of getting a formula I could use to calculate them. --Belg4mit 02:44, 31 January 2007 (UTC)
[edit] Needs improvement
I feel the explanations offered here are too brief to be of use to anyone who doesn't already know what p-values are. What exactly is 'impressive' supposed to mean in this context to a lay person? General improvement in the clarity of language used, number of examples, and adding calculations would benefit this page.
