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Original Research claim towards the examples[edit]

I do not agree that the examples should be removed. They are examples, so cannot be original research. And they are very useful to illustrate the concept. I am grateful to the person who added them. I do not think that every illustration of a mathematical concept has to be justified by a reference to a textbook where it appears. — Preceding unsigned comment added by (talk) 09:55, 24 September 2015 (UTC)

I was planning to write just about what wrote above. That is, that they are examples to make it easier to understand, but are not original research. They don't try to learn anything that wasn't known, but only make it easier for others to understand what is known. As many parts of statistics are counterintuitive, these examples can be very useful. Gah4 (talk) 00:47, 12 December 2015 (UTC)

The misunderstandings section is redundant and confusing[edit]

I don't think we need three different statements to say "the p-value is not the probability that the null hypothesis is true," "the p-value is not the probability that the alternative hypothesis is false," and "the p-value is not the probability that a finding is just a fluke." When a p-value is significant, all those statements are essentially the same. Some of the wording is also very unclear and Bayesian statistics are invoked for seemingly no reason (you don't need Bayes to explain why p-values aren't the probability that a finding is a fluke). I suggest simplifying the section if not scrapping it entirely. — Preceding unsigned comment added by (talk) 07:24, 24 September 2015 (UTC)

P.S. Speaking of redundancy, the phrase "sure certainty" surely and for certain needs to go. For sure. — Preceding unsigned comment added by (talk) 07:28, 24 September 2015 (UTC)

I think it should be kept. It is hard to understand, which is why the examples of misunderstanding are useful. A little redundancy helps drive home the point. The subject is confusing, which is the reason the section is there. Gah4 (talk) 00:50, 12 December 2015 (UTC)

As clear as mud![edit]

To the non-statistician, the terminology ('disprove the null hypothesis') is unclear. The article as it stands (December 2015) is written in the language and demeanor of a statistics text. It would be improved with ... examples ... plain-English explanations... — Preceding unsigned comment added by (talk) 13:48, 14 December 2015 (UTC)

Please correct article![edit]

Yes, the article is not didactic (!!) for non-statistician, and shows absense of focus on usual reader needs, is not encyclopedic without better structure and simplifications.

The lead section needs a basic summary (and this 3 assertions can be reused in all article). EXAMPLE:

  • A small p-value (typically ≤ 0.05) indicates strong evidence against the null hypothesis. The null hypothesis was reject, is good.
  • A large p-value (> 0.05) indicates weak evidence against the null hypothesis. Fail to reject the null hypothesis, is bad.
  • p-values very close to the cutoff (~ 0.05) are considered to be marginal (could go either way)...

Statistical analysis must always report the p-value, so readers can draw their own conclusions.

--Krauss (talk) 06:18, 30 January 2016 (UTC)