Sequential Analysis
E212551
Sequential Analysis is a foundational statistical methodology that develops procedures for evaluating data as it is collected, allowing decisions to be made at variable sample sizes rather than after a fixed number of observations.
All labels observed (2)
| Label | Occurrences |
|---|---|
| Sequential Analysis canonical | 2 |
| Wald sequential probability ratio test | 1 |
How this entity was disambiguated
This entity first appeared as the object of triple T1902492 — 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: Sequential Analysis Context triple: [Abraham Wald, notableWork, Sequential Analysis]
-
A.
Markov processes
Markov processes are stochastic processes in which the future evolution depends only on the present state and not on the past history.
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B.
Innovations approach to detection and estimation
"Innovations approach to detection and estimation" is a seminal work by Thomas Kailath that develops a powerful stochastic framework for solving signal detection and parameter estimation problems, particularly in control and communication systems.
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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.
Extrapolation, Interpolation, and Smoothing of Stationary Time Series
"Extrapolation, Interpolation, and Smoothing of Stationary Time Series" is a foundational mathematical work by Norbert Wiener that developed the theory of optimal prediction and filtering for stationary stochastic processes, laying the groundwork for modern signal processing and control theory.
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E.
Bayesian inference
Bayesian inference is a statistical framework that updates the probability of hypotheses as more evidence or data becomes available, using Bayes’ theorem to combine prior beliefs with observed information.
- 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: Sequential Analysis Target entity description: Sequential Analysis is a foundational statistical methodology that develops procedures for evaluating data as it is collected, allowing decisions to be made at variable sample sizes rather than after a fixed number of observations.
-
A.
Markov processes
Markov processes are stochastic processes in which the future evolution depends only on the present state and not on the past history.
-
B.
Innovations approach to detection and estimation
"Innovations approach to detection and estimation" is a seminal work by Thomas Kailath that develops a powerful stochastic framework for solving signal detection and parameter estimation problems, particularly in control and communication systems.
-
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.
Extrapolation, Interpolation, and Smoothing of Stationary Time Series
"Extrapolation, Interpolation, and Smoothing of Stationary Time Series" is a foundational mathematical work by Norbert Wiener that developed the theory of optimal prediction and filtering for stationary stochastic processes, laying the groundwork for modern signal processing and control theory.
-
E.
Bayesian inference
Bayesian inference is a statistical framework that updates the probability of hypotheses as more evidence or data becomes available, using Bayes’ theorem to combine prior beliefs with observed information.
- F. None of above. chosen
Statements (48)
| Predicate | Object |
|---|---|
| instanceOf |
decision procedure
ⓘ
statistical inference method ⓘ statistical methodology ⓘ |
| aimsTo | optimize trade-off between sample size and decision accuracy ⓘ |
| appliesTo |
A/B testing
ⓘ
clinical trials ⓘ industrial experimentation ⓘ online experimentation ⓘ quality control ⓘ reliability testing ⓘ sequential monitoring of processes ⓘ |
| assumes |
data arrive in a temporal or logical sequence
ⓘ
pre-specified stopping rules ⓘ |
| contrastsWith | fixed-sample-size analysis ⓘ |
| developedBy | Abraham Wald ⓘ |
| fieldOfStudy |
sequential statistics
ⓘ
statistics ⓘ |
| hasProperty |
allows continuation until a decision boundary is reached
ⓘ
allows early stopping for efficacy ⓘ allows early stopping for futility ⓘ can reduce average sample size compared to fixed-sample tests ⓘ controls long-run error rates under specified designs ⓘ does not require a fixed sample size in advance ⓘ |
| hasPurpose |
to allow decisions at variable sample sizes
ⓘ
to control error probabilities while monitoring data sequentially ⓘ to evaluate data as it is collected ⓘ to improve efficiency of statistical testing ⓘ |
| influencedBy |
Neyman–Pearson hypothesis testing framework
ⓘ
linked to:
Neyman–Pearson theory of hypothesis testing
|
| relatedTo |
Bayesian sequential decision theory
ⓘ
adaptive clinical trial design ⓘ alpha spending function ⓘ group sequential design ⓘ multi-armed bandit problem ⓘ sequential Monte Carlo methods ⓘ |
| requires | adjustment for repeated looks at the data ⓘ |
| usedFor |
monitoring safety and efficacy in ongoing studies
ⓘ
real-time decision making based on accumulating data ⓘ |
| usesConcept |
boundary crossing
ⓘ
expected sample size ⓘ interim analysis ⓘ likelihood ratio ⓘ power of a test ⓘ sequential confidence interval ⓘ sequential hypothesis testing ⓘ sequential probability ratio test ⓘ stopping rule ⓘ type I error ⓘ type II error ⓘ |
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: Sequential Analysis Description of subject: Sequential Analysis is a foundational statistical methodology that develops procedures for evaluating data as it is collected, allowing decisions to be made at variable sample sizes rather than after a fixed number of observations.
Referenced by (3)
Full triples — surface form annotated when it differs from this entity's canonical label.