Talk:Estimation statistics

This page should not be speedy deleted because...

Dear Ujjwal, This page should not be speedily deleted because the page is not a duplication of Estimation Theory, but covers a subfield of statistics that is otherwise not described by Wikipedia.

Please reinstate the page.

This page should not be speedy deleted because...

This page should not be speedily deleted because it is not a duplication of estimation theory, but is the only article in Wikipedia that covers this important subfield of statistics. — Preceding unsigned comment added by Upisaida (talk • contribs) 05:54, 25 October 2013 (UTC)

Is this notable enough for its own article?

There doesn't seem to be much of note here that could not be better placed as a specific subsection of the Estimation theory or the Interval estimation article, of which most of this would be a fairly modest variation on (it is relevant to the practice of statistics in psychology and related areas but statistically it doesn't say much that is actually theoretically new); the current emphasis in the article makes it seem like a practice-of-psychology-research article rather than a statistics one. If it is to remain separate the title should be more clearly terminologically distinct from statistical estimation (e.g. "Cohen's estimation statistics" or by explicitly mentioning application areas in the title) and it should clearly reference/link to the statistical theory in which it is based (which may require some attention from someone sufficiently familiar with the statistical literature). In that case I could see some argument for keeping it as a stats article yet separated from the abovementioned articles. Glenbarnett (talk) 00:03, 23 August 2017 (UTC)

Introduction: "the point of hypothesis testing"

... is not getting a p-value. The point of hypothesis testing is to deal with an inference question like "is this effect different from zero?". By contrast, estimation is related to "how much". P-values are a means to an end, not an end in themselves, and in any case this completely ignores the various forms of testing not based on p-values; while p-values are essential to a Fisherian view of hypothesis testing, p-values are not part of the formal Neyman-Pearson framework (though they can be used there as a different way of framing a rejection rule, they're completely unnecessary to it), they're not relevant to testing in a Bayesian framework, and so on. Glenbarnett (talk) 00:03, 23 August 2017 (UTC)

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