Student’s t-distribution
E596524
Student’s t-distribution is a continuous probability distribution used primarily to estimate population means and conduct hypothesis tests when sample sizes are small and population variance is unknown.
All labels observed (2)
| Label | Occurrences |
|---|---|
| Student's t-distribution | 2 |
| Student’s t-distribution canonical | 2 |
How this entity was disambiguated
This entity first appeared as the object of triple T6482583 — 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.
Target entity: Student’s t-distribution Context triple: [Gamma function, appearsIn, Student’s t-distribution]
-
A.
Hotelling’s T-squared distribution
Hotelling’s T-squared distribution is a multivariate generalization of Student’s t-distribution used primarily for hypothesis testing and constructing confidence regions for mean vectors in multivariate statistics.
-
B.
Tukey's lambda distribution
Tukey's lambda distribution is a flexible family of probability distributions used primarily for exploratory data analysis and modeling diverse shapes of data, including varying degrees of skewness and kurtosis.
-
C.
F-distribution
The F-distribution is a continuous probability distribution widely used in statistics, especially for comparing variances and conducting analysis of variance (ANOVA) tests.
-
D.
Tukey's honestly significant difference test
Tukey's honestly significant difference test is a statistical post-hoc procedure used to determine which specific group means differ after an ANOVA indicates a significant overall effect.
-
E.
Tukey's biweight
Tukey's biweight is a robust statistical estimator that downweights outliers to provide resistant measures of central tendency or regression fits.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Target entity: Student’s t-distribution Target entity description: Student’s t-distribution is a continuous probability distribution used primarily to estimate population means and conduct hypothesis tests when sample sizes are small and population variance is unknown.
-
A.
Hotelling’s T-squared distribution
Hotelling’s T-squared distribution is a multivariate generalization of Student’s t-distribution used primarily for hypothesis testing and constructing confidence regions for mean vectors in multivariate statistics.
-
B.
Tukey's lambda distribution
Tukey's lambda distribution is a flexible family of probability distributions used primarily for exploratory data analysis and modeling diverse shapes of data, including varying degrees of skewness and kurtosis.
-
C.
F-distribution
The F-distribution is a continuous probability distribution widely used in statistics, especially for comparing variances and conducting analysis of variance (ANOVA) tests.
-
D.
Tukey's honestly significant difference test
Tukey's honestly significant difference test is a statistical post-hoc procedure used to determine which specific group means differ after an ANOVA indicates a significant overall effect.
-
E.
Tukey's biweight
Tukey's biweight is a robust statistical estimator that downweights outliers to provide resistant measures of central tendency or regression fits.
- F. None of above. chosen
Statements (44)
| Predicate | Object |
|---|---|
| instanceOf |
location-scale family
ⓘ
probability distribution ⓘ |
| alsoKnownAs |
Student t-distribution
NERFINISHED
ⓘ
t-distribution NERFINISHED ⓘ |
| appearsIn | Bayesian posterior for mean with unknown variance under conjugate priors ⓘ |
| arisesFrom | ratio of standard normal variable to square root of scaled chi-square variable ⓘ |
| belongsTo | location-scale t-family ⓘ |
| convergesTo | standard normal distribution as degrees of freedom go to infinity ⓘ |
| cumulativeDistributionFunction | expressible via incomplete beta function ⓘ |
| definedBy | degrees of freedom parameter ν>0 ⓘ |
| familyMemberOf | elliptical distributions ⓘ |
| hasHeavierTailsThan | normal distribution ⓘ |
| introducedBy | William Sealy Gosset NERFINISHED ⓘ |
| introducedInYear | 1908 ⓘ |
| introducedUnderPseudonym | Student NERFINISHED ⓘ |
| isScaleMixtureOf | normal distributions ⓘ |
| isSymmetricAbout | 0 ⓘ |
| kurtosisExcess | 6/(ν-4) for ν>4 ⓘ |
| limitingCase | standard normal distribution as ν→∞ ⓘ |
| mean | 0 for degrees of freedom greater than 1 ⓘ |
| median | 0 ⓘ |
| mode | 0 ⓘ |
| namedAfter | William Sealy Gosset NERFINISHED ⓘ |
| parameter | degrees of freedom ⓘ |
| probabilityDensityFunction | f(x)=Γ((ν+1)/2)/(√(νπ)Γ(ν/2))·(1+x²/ν)^(-(ν+1)/2) ⓘ |
| relatedDistribution |
Cauchy distribution
NERFINISHED
ⓘ
F-distribution NERFINISHED ⓘ chi-square distribution ⓘ |
| skewness | 0 for ν>3 ⓘ |
| specialCase | Cauchy distribution when ν=1 ⓘ |
| sufficientStatisticContext | sample mean with unknown variance in normal model ⓘ |
| support | all real numbers ⓘ |
| tailBehavior | polynomial decay ⓘ |
| usedFor |
confidence intervals for means
ⓘ
inference on population mean with unknown variance ⓘ one-sample t-test ⓘ paired t-test ⓘ regression coefficient inference ⓘ two-sample t-test ⓘ |
| usedWhen |
population variance is unknown
ⓘ
sample size is small ⓘ |
| variance |
infinite for 1<ν≤2
ⓘ
undefined for ν≤1 ⓘ ν/(ν-2) for degrees of freedom greater than 2 ⓘ |
How these facts were elicited
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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.
Subject: Student’s t-distribution Description of subject: Student’s t-distribution is a continuous probability distribution used primarily to estimate population means and conduct hypothesis tests when sample sizes are small and population variance is unknown.
Referenced by (4)
Full triples — surface form annotated when it differs from this entity's canonical label.