Spearman rank-order correlation coefficient
E622306
correlation coefficient
measure of statistical dependence
nonparametric statistic
rank correlation coefficient
The Spearman rank-order correlation coefficient is a nonparametric statistical measure that assesses the strength and direction of a monotonic relationship between two ranked variables.
All labels observed (2)
| Label | Occurrences |
|---|---|
| Spearman rank correlation | 1 |
| Spearman rank-order correlation coefficient canonical | 1 |
How this entity was disambiguated
This entity first appeared as the object of triple T6824191 — 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: Spearman rank-order correlation coefficient Context triple: [Charles Spearman, knownFor, Spearman rank-order correlation coefficient]
-
A.
Fisher's exact test
Fisher's exact test is a statistical significance test used to determine whether there are nonrandom associations between two categorical variables in a contingency table, especially with small sample sizes.
-
B.
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.
-
C.
Kruskal–Wallis test
The Kruskal–Wallis test is a nonparametric statistical method used to determine whether there are statistically significant differences between the medians of three or more independent groups.
-
D.
PairWise Rankings
PairWise Rankings is a statistical system used to compare and rank NCAA Division I men's ice hockey teams, closely mirroring the criteria used for at-large selection to the national tournament.
-
E.
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.
- 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: Spearman rank-order correlation coefficient Target entity description: The Spearman rank-order correlation coefficient is a nonparametric statistical measure that assesses the strength and direction of a monotonic relationship between two ranked variables.
-
A.
Fisher's exact test
Fisher's exact test is a statistical significance test used to determine whether there are nonrandom associations between two categorical variables in a contingency table, especially with small sample sizes.
-
B.
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.
-
C.
Kruskal–Wallis test
The Kruskal–Wallis test is a nonparametric statistical method used to determine whether there are statistically significant differences between the medians of three or more independent groups.
-
D.
PairWise Rankings
PairWise Rankings is a statistical system used to compare and rank NCAA Division I men's ice hockey teams, closely mirroring the criteria used for at-large selection to the national tournament.
-
E.
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.
- F. None of above. chosen
Statements (49)
| Predicate | Object |
|---|---|
| instanceOf |
correlation coefficient
ⓘ
measure of statistical dependence ⓘ nonparametric statistic ⓘ rank correlation coefficient ⓘ |
| alsoKnownAs |
Spearman rank correlation
NERFINISHED
ⓘ
Spearman’s rho NERFINISHED ⓘ |
| appliesTo |
ranked variables
ⓘ
two variables ⓘ |
| assumes | monotonic relationship ⓘ |
| basedOn | ranks of data ⓘ |
| calculatedFrom | differences between paired ranks ⓘ |
| category |
descriptive statistics
ⓘ
inferential statistics ⓘ |
| comparedWith | Pearson product-moment correlation coefficient NERFINISHED ⓘ |
| dataType |
continuous data converted to ranks
ⓘ
ordinal data ⓘ |
| doesNotAssume |
linear relationship
ⓘ
normal distribution of variables ⓘ |
| domain |
nonparametric statistics
ⓘ
statistics ⓘ |
| handles | ties in ranks with tie-correction procedures ⓘ |
| introducedBy | Charles Spearman NERFINISHED ⓘ |
| introducedInYear | 1904 ⓘ |
| isNonparametricVersionOf | Pearson correlation coefficient NERFINISHED ⓘ |
| measures |
direction of monotonic relationship
ⓘ
strength of monotonic relationship ⓘ |
| namedAfter | Charles Spearman NERFINISHED ⓘ |
| rangeEndpoint |
-1 indicates perfect negative monotonic association
ⓘ
0 indicates no monotonic association ⓘ 1 indicates perfect positive monotonic association ⓘ |
| relatedConcept |
Kendall rank correlation coefficient
NERFINISHED
ⓘ
monotonic function ⓘ ordinal correlation ⓘ |
| requires | paired observations ⓘ |
| robustTo | outliers in raw data ⓘ |
| symbol |
rs
ⓘ
ρs ⓘ |
| usedFor |
assessing monotonic association
ⓘ
correlation analysis with non-normal data ⓘ correlation analysis with ordinal data ⓘ measuring rank correlation ⓘ testing statistical independence ⓘ |
| usedIn |
biostatistics
ⓘ
econometrics ⓘ machine learning feature analysis ⓘ psychometrics NERFINISHED ⓘ social sciences ⓘ |
| usedWith | Spearman rank correlation test NERFINISHED ⓘ |
| valueRange | -1 to 1 ⓘ |
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: Spearman rank-order correlation coefficient Description of subject: The Spearman rank-order correlation coefficient is a nonparametric statistical measure that assesses the strength and direction of a monotonic relationship between two ranked variables.
Referenced by (2)
Full triples — surface form annotated when it differs from this entity's canonical label.
this entity surface form:
Spearman rank correlation