statistical inference

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statistical inference

[stə′tis·tə·kəl ′in·frəns]
(statistics)
The process of reaching conclusions concerning a population upon the basis of random samplings.

statistical inference

see PROBABILITY, STATISTICS.
References in periodicals archive ?
e., Baker, 2016; Munafo et al., 2014, 2017; Nuzzo, 2015), statistical tools with these properties will help researchers when making statistical inferences. We have also observed that, although the Vovk-Sellke p-value calibration is better than the p-value to orient researchers' decisions about the null hypothesis, the BIC-based Bayes Factor is more accurate in the situations we simulated.
In Bayesian statistical inference, the loss function plays an important role, and symmetric loss function, such as the squared error loss L([??], [theta]) = [([??] - [theta]).sup.2] is widely used, which assigns equal losses to overestimation and underestimation.
Statistical inference is one of the big ideas in statistics, but formal applications of inference (hypothesis testing, parameter estimation) are highly complex and usually not taught until university.
As an application of the statistical inferences developed in this paper, I reinvestigate the issue of working wives and U.S.
and, can we draw statistical inferences about that population?
For the moment we can continue to put issues of statistical inference to one side.
With this package, students can learn how to develop an abstract model of a real-life situation, to write a computer program simulating that model and to draw statistical inferences from the resulting data.
Their common fundamental belief appears to be that statistical inference is the correct data science tool necessary for appraisal work.
The document contains no statistical analysis; therefore, statistical inferences of data is not intended in this document.
He covers stream of variation (SoV) modeling by reviewing matrix theory and multivariate statistics, including multivariate distribution and properties and statistical inferences on mean vectors and linear models, describing variation propagation modeling with applications in assembling and machining processes, including model validation and a factor analysis method for variability modeling, diagnosing the source of variation, including diagnosis through variation pattern matching and estimation, using design methods to reduce variability, including optimized fixture layout design and process-oriented tolerance synthesis, and building in quality and reliability.
Our specific aim is to provide Delta-method-based asymptotic statistical inference for the mediation effect feasible to test large numbers of probes simultaneously.
The Bayesian model of statistical inference provides a unified solution to these two distinct problems of quantitative comparative research.

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