Béda révisi "Ajén-P"

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2 bita ditambahkeun ,  3 tahun yang lalu
m
Ngarapihkeun éjahan, replaced: nyaeta → nyaéta, yen → yén (2), ea → éa using AWB
m (bot Miceun: fa:پی - مقدار (strong connection between (2) su:Ajén-P and fa:پی-مقدار))
m (Ngarapihkeun éjahan, replaced: nyaeta → nyaéta, yen → yén (2), ea → éa using AWB)
Dina [[statistik]], '''nilai-p''' tina variabel random T nyaetanyaéta [[téori probabilitas|probabilitas]] Pr(T ≤ t<sub>observed</sub>) numana T bakal dianggap leuwih gede atawa sarua jeung nilai observasi t<sub>observed</sub>, dina kayaan [[null hypothesis]] dianggap bener.
 
Dina basa sejen, anggapan yenyén null hypothesis sederhana ditolak lamun tes [[statistic]] ''T'' leuwih gede tinimbang nilai kritis ''c''. Kira-kira dina sabageansabagéan kasus T nu di-observasi sarua jeung t<sub>observed</sub>. Mangka nilai-p tina T dina etaéta kasus probabiliti yenyén T bakal sarua atawa leuwih ti t<sub>observed</sub>.
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The p-value does not depend on unobservable parameters, but only on the data, i.e., it is observable; it is a "statistic." In classical frequentist inference, one rejects the null hypothesis if the p-value is smaller than a number called the ''level'' of the test. In effect, the p-value itself is then being used as the test statistic. If the level is 0.05, then the probability that the p-value is less than 0.05, given that the null hypothesis is true, is 0.05, provided the test statistic has a continuous distribution. In that case, the p-value is [[sebaran seragam|uniformly distributed]] if the null hypothesis is true.
==Frequent misunderstandings==
 
There are several common misunderstandings about p-values. All of the following statements are '''FALSE''':
 
a) The p-value is the probability that the [[null hypothesis]] is true, justifying the "rule" of considering as significant p-values closer to 0 (zero).
 
Comment: In fact, [[frequentism|frequentist statistics]] does not, and cannot, attach probabilities to hypotheses. Comparison of Bayesian and classical approaches shows that p can be very close to zero while the posterior probability of the null is very close to unity. This is the '''Jeffreys-Lindley Paradox'''.
== Sumber Rujukan ==
* Sellke, T., M.J. Bayarri, & J. Berger. 2001. Calibration of P-values for Testing Precise Null Hypotheses. ''Am. Statistician'' 55: 62-71.
 
 
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