Statement on p-values and statistical significance
E728779
The "Statement on p-values and statistical significance" is a landmark American Statistical Association document that clarifies the proper use and interpretation of p-values and cautions against their misuse in scientific research and decision-making.
All labels observed (1)
| Label | Occurrences |
|---|---|
| Statement on p-values and statistical significance canonical | 1 |
How this entity was disambiguated
This entity first appeared as the object of triple T8349495 — resolving that mention is where its identity was fixed. The disambiguator weighed these candidate entities and picked the highlighted one (or “None”, minting a new entity). This is how homonymy is resolved: the same surface form can point to different entities.
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Statement on p-values and statistical significance Context triple: [American Statistical Association, notableWork, Statement on p-values and statistical significance]
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A.
Neyman–Pearson theory of hypothesis testing
The Neyman–Pearson theory of hypothesis testing is a foundational statistical framework that formalizes how to construct and evaluate tests for competing hypotheses using concepts like Type I and Type II errors and power.
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B.
Statistical Methods for Research Workers
Statistical Methods for Research Workers is a foundational 1925 statistics textbook by Ronald A. Fisher that helped establish modern statistical theory and practice in scientific research.
-
C.
“Statistical Confluence Analysis by Means of Complete Regression Systems”
“Statistical Confluence Analysis by Means of Complete Regression Systems” is a foundational econometric work by Ragnar Frisch that develops a systematic regression-based framework for analyzing interdependent economic relationships.
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D.
A Solution to the Ecological Inference Problem
A Solution to the Ecological Inference Problem is a influential methodological book by political scientist Gary King that introduces statistical techniques for inferring individual-level behavior from aggregate data.
-
E.
Illustrations of the Logic of Science
Illustrations of the Logic of Science is a series of influential essays by Charles Sanders Peirce that helped lay the foundations of modern logic, scientific methodology, and pragmatism.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Statement on p-values and statistical significance Target entity description: The "Statement on p-values and statistical significance" is a landmark American Statistical Association document that clarifies the proper use and interpretation of p-values and cautions against their misuse in scientific research and decision-making.
-
A.
Neyman–Pearson theory of hypothesis testing
The Neyman–Pearson theory of hypothesis testing is a foundational statistical framework that formalizes how to construct and evaluate tests for competing hypotheses using concepts like Type I and Type II errors and power.
-
B.
Statistical Methods for Research Workers
Statistical Methods for Research Workers is a foundational 1925 statistics textbook by Ronald A. Fisher that helped establish modern statistical theory and practice in scientific research.
-
C.
“Statistical Confluence Analysis by Means of Complete Regression Systems”
“Statistical Confluence Analysis by Means of Complete Regression Systems” is a foundational econometric work by Ragnar Frisch that develops a systematic regression-based framework for analyzing interdependent economic relationships.
-
D.
A Solution to the Ecological Inference Problem
A Solution to the Ecological Inference Problem is a influential methodological book by political scientist Gary King that introduces statistical techniques for inferring individual-level behavior from aggregate data.
-
E.
Illustrations of the Logic of Science
Illustrations of the Logic of Science is a series of influential essays by Charles Sanders Peirce that helped lay the foundations of modern logic, scientific methodology, and pragmatism.
- F. None of above. chosen
Statements (48)
| Predicate | Object |
|---|---|
| instanceOf |
American Statistical Association statement
ⓘ
guidance document ⓘ scientific position statement ⓘ |
| accessMode | open access ⓘ |
| aimsTo |
clarify proper use of p-values
ⓘ
discourage misuse of p-values ⓘ improve statistical practice in science ⓘ |
| clarifies |
a p-value near 0.05 should not be treated as a strict decision rule
ⓘ
p-values do not measure the probability that the data were produced by random chance alone ⓘ p-values do not measure the probability that the studied hypothesis is true ⓘ statistical significance does not imply scientific or practical importance ⓘ |
| countryOfOrigin |
United States
ⓘ
linked to:
United States of America
|
| datePublished | 2016 ⓘ |
| describedAs | landmark ASA document on p-values ⓘ |
| documentType | position paper ⓘ |
| emphasizes |
importance of data quality
ⓘ
importance of full reporting of results ⓘ importance of study design ⓘ importance of transparency in analysis ⓘ |
| field |
research methodology
ⓘ
statistical inference ⓘ statistics ⓘ |
| hasPart | six principles on the use and interpretation of p-values ⓘ |
| influenced |
debates on reproducibility in science
ⓘ
guidelines for statistical practice in multiple disciplines ⓘ journal editorial policies on statistical reporting ⓘ |
| language | English ⓘ |
| mainSubject |
hypothesis testing
ⓘ
p-value ⓘ reproducible research ⓘ statistical significance ⓘ |
| publisher | American Statistical Association ⓘ |
| recommends |
considering effect sizes
ⓘ
considering prior evidence and plausibility ⓘ using confidence intervals ⓘ using other measures of evidence ⓘ |
| relatedTo |
ASA special issue on statistical inference in The American Statistician
ⓘ
linked to:
The American Statistician
discussions on moving beyond p<0.05 ⓘ |
| targetAudience |
journal editors
ⓘ
policy makers ⓘ scientific researchers ⓘ statisticians ⓘ |
| warnsAgainst |
data dredging
ⓘ
mechanical use of bright-line significance thresholds ⓘ p-hacking ⓘ selective reporting based on p-values ⓘ using p-values as a measure of effect size ⓘ using p-values as a measure of evidence by themselves ⓘ |
How these facts were elicited
The pipeline generated the facts above by prompting gpt-5.1 with this entity's name + description and the instruction below.
Instruction
You are a knowledge base construction expert. Given a subject entity and a description of it, return factual statements that you know for the subject as a JSON list of dictionaries(triples), where keys must be "subject", "predicate" and "object". The number of facts may be very high, between 25 to 50 or more, for very popular subjects. For less popular subjects, the number of facts can be very low, like 5 or 10. # Requirements - If you don't know the subject at all, return an empty list. - If the subject is not a named entity, return an empty list. - Include at least one triple where predicate is "instanceOf". - Do not get too wordy. - Separate several objects into multiple triples with one object.
Input
Subject: Statement on p-values and statistical significance Description of subject: The "Statement on p-values and statistical significance" is a landmark American Statistical Association document that clarifies the proper use and interpretation of p-values and cautions against their misuse in scientific research and decision-making.
Referenced by (1)
Full triples — surface form annotated when it differs from this entity's canonical label.
American Statistical Association
→
notableWork
→
Statement on p-values and statistical significance
ⓘ