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Dina [[statistik]], '''nilai-p''' tina variabel random T nyaeta [[téori probabilitas|probabilitas]] Pr(T &le; 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 [[statistik]], '''nilai-p''' tina variabel random T nyaeta [[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 yen null hypothesis sederhana ditolak lamun tes [[statistic]] ''T'' leuwih gede tinimbang nilai kritis ''c''. Kira-kira dina sabagean kasus T nu di-observasi sarua jeung t<sub>observed</sub>. Mangka nilai-p tina T dina eta kasus probabiliti yen T bakal sarua atawa leuwih ti t<sub>observed</sub>.
Dina basa sejen, anggapan yen null hypothesis sederhana ditolak lamun tes [[statistic]] ''T'' leuwih gede tinimbang nilai kritis ''c''. Kira-kira dina sabagean kasus T nu di-observasi sarua jeung t<sub>observed</sub>. Mangka nilai-p tina T dina eta kasus probabiliti yen 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.
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.
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c) The p-value is the probability that a replicating experiment would not yield the same conclusion.
c) The p-value is the probability that a replicating experiment would not yield the same conclusion.
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==Sumber Rujukan==
== Sumber Rujukan ==
*Sellke, T., M.J. Bayarri, & J. Berger. 2001. Calibration of P-values for Testing Precise Null Hypotheses. ''Am. Statistician'' 55: 62-71.
* 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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[[fa:پی - مقدار]]
[[fa:پی - مقدار]]
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[[fi:P-arvo]]
[[fr:P-value]]
[[fr:Valeur p]]
[[it:Valore p]]
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[[ko:유의확률]]
[[ko:유의확률]]

Révisi nurutkeun 7 April 2010 03.28

Dina statistik, nilai-p tina variabel random T nyaeta probabilitas Pr(T ≤ tobserved) numana T bakal dianggap leuwih gede atawa sarua jeung nilai observasi tobserved, dina kayaan null hypothesis dianggap bener.

Dina basa sejen, anggapan yen null hypothesis sederhana ditolak lamun tes statistic T leuwih gede tinimbang nilai kritis c. Kira-kira dina sabagean kasus T nu di-observasi sarua jeung tobserved. Mangka nilai-p tina T dina eta kasus probabiliti yen T bakal sarua atawa leuwih ti tobserved.

Sumber Rujukan

  • Sellke, T., M.J. Bayarri, & J. Berger. 2001. Calibration of P-values for Testing Precise Null Hypotheses. Am. Statistician 55: 62-71.