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- P-value abstract "In statistical significance testing, the p-value is the probability of obtaining a test statistic result at least as extreme as the one that was actually observed, assuming that the null hypothesis is true. A researcher will often "reject the null hypothesis" when the p-value turns out to be less than a certain significance level, often 0.05 or 0.01. Such a result indicates that the observed result would be highly unlikely under the null hypothesis. Many common statistical tests, such as chi-squared tests or Student's t-test, produce test statistics which can be interpreted using p-values. In a statistical test, the p-value is the probability of getting the same value for a model built around two hypotheses, one is the "neutral" (or "null") hypothesis, the other is the hypothesis under testing. If this p-value is less than or equal to the threshold value previously set (traditionally 5% or 1% ), one rejects the neutral hypothesis and accepts the test hypothesis as valid .An informal interpretation with a significance level of about 10%: : very strong presumption against neutral hypothesis : very strong presumption against neutral hypothesis : strong presumption against neutral hypothesis low presumption against neutral hypothesis no presumption against the neutral hypothesisA new Bayesian inference approach highlights that these threshold values are too optimistic and explain the lack of reproducibility of scientific studies, suggesting a p < 0.001 or 0.0053. The p-value is a key concept in the approach of Ronald Fisher, where he uses it to measure the weight of the data against a specified hypothesis, and as a guideline to ignore data that does not reach a specified significance level. Fisher's approach does not involve any alternative hypothesis, which is instead a feature of the Neyman–Pearson approach. The p-value should not be confused with the significance level α in the Neyman–Pearson approach or the Type I error rate [false positive rate]. The level α is sometimes called the "nominal significance level" and it is the theoretical probability that the null hypothesis is rejected when it is in fact true and all distributional assumptions for the test are satisfied. This α is fixed in advance of the test and it is the threshold value (often 0.05) to which the p-value is compared. In repeated sampling, the p-value varies and, when the null hypothesis is true, an expected proportion 1-α of the tests are non-significant, while α of the tests are significant. However, in practice it may happen that the distributional assumptions for a given test do not hold. The Type I error rate is the true probability that the null hypothesis is rejected when it is true, sometimes called the achieved significance level, which for some test procedures may differ from the nominal rate. A good test should control the Type I error rate at the nominal significance level. Fundamentally, the p-value does not in itself support reasoning about the probabilities of hypotheses, nor choosing between different hypotheses–it is simply a measure of how likely the data (or a more "extreme" version of it) were to have occurred by chance, assuming the null hypothesis is true.Statistical hypothesis tests making use of p-values are commonly used in many fields of science and social sciences, such as economics, psychology, biology, criminal justice and criminology, and sociology.Depending on which style guide is applied, the "p" is styled either italic or not, capitalized or not, and hyphenated or not (p-value, p value, P-value, P value, p-value, p value, P-value, P value).".
- P-value thumbnail P-value_Graph.png?width=300.
- P-value wikiPageExternalLink 03-26.pdf.
- P-value wikiPageExternalLink chap3.htm.
- P-value wikiPageExternalLink chap4.htm.
- P-value wikiPageExternalLink tabIII.gif.
- P-value wikiPageExternalLink IFSPM.pdf.
- P-value wikiPageExternalLink c14.
- P-value wikiPageExternalLink p05.htm.
- P-value wikiPageExternalLink p-values.html.
- P-value wikiPageExternalLink LHSP.HTM.
- P-value wikiPageExternalLink twelve+P+value+misconceptions.pdf.
- P-value wikiPageID "554994".
- P-value wikiPageRevisionID "606195884".
- P-value hasPhotoCollection P-value.
- P-value subject Category:Hypothesis_testing.
- P-value subject Category:Statistical_terminology.
- P-value comment "In statistical significance testing, the p-value is the probability of obtaining a test statistic result at least as extreme as the one that was actually observed, assuming that the null hypothesis is true. A researcher will often "reject the null hypothesis" when the p-value turns out to be less than a certain significance level, often 0.05 or 0.01. Such a result indicates that the observed result would be highly unlikely under the null hypothesis.".
- P-value label "P-Wert".
- P-value label "P-value".
- P-value label "P-waarde".
- P-value label "P-wartość".
- P-value label "P-значение".
- P-value label "Valeur p".
- P-value label "Valor P".
- P-value label "Valor-p".
- P-value label "Valore p".
- P-value label "قيمة احتمالية".
- P-value sameAs P-Wert.
- P-value sameAs Valor_P.
- P-value sameAs Valeur_p.
- P-value sameAs Valore_p.
- P-value sameAs 유의_확률.
- P-value sameAs P-waarde.
- P-value sameAs P-wartość.
- P-value sameAs Valor-p.
- P-value sameAs m.02pgmn.
- P-value sameAs Q253255.
- P-value sameAs Q253255.
- P-value wasDerivedFrom P-value?oldid=606195884.
- P-value depiction P-value_Graph.png.
- P-value isPrimaryTopicOf P-value.
