statistical inference

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Statistical inference

A statistical method of drawing conclusions on unknown properties of a population based on a random sampling of data from that population.

statistical inference

a process by which we infer conclusions about a statistical POPULATION from which only a SAMPLE has been drawn. For example, if one million Britons buy bicycles each year, and 200 are asked why they do so, 50% may say because it helps to keep them fit. From this sample one may infer that 50% of the total population of one million Britons buying bicycles do so for this reason. However, it is not possible to say with 100% accuracy that this is the case unless the views of all one million were obtained. Nevertheless, it is possible to say with reasonable confidence that the estimation of 50% is correct for the whole population.
References in periodicals archive ?
Conventional statistical inferences (such as standard error, f-tests, etc.
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.
An informal statistical inference is a claim (a conclusion such as a prediction, estimate or generalisation) with three characteristics:
By means of simulation studies we examine which of the approaches--the GLIM, GLMM with marginal MLE, or GLMM with PQLE--provides more reliable statistical inferences about p.
We examined the robustness of statistical inferences about [rho] to the normal approximation for [delta] (see Simulations section) when [delta] is actually a log ratio of gamma random variables.
As an application of the statistical inferences developed in this paper, I reinvestigate the issue of working wives and U.
5 Zheng and Cushing (1997) provide statistical inferences for testing summary inequality measures (the Theil measures and the Gini coefficient) with dependent samples.
Moreover, students learn important lessons about statistical inference from such an exercise.
Their common fundamental belief appears to be that statistical inference is the correct data science tool necessary for appraisal work.
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.
The Effects of Inter-Gene Associations on Statistical Inferences From Microarray Data (Kerby Shedden).
Chapter 10 Statistical Inferences about Two Populations.

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