statistical inference

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Related to statistical inference: Statistical hypothesis

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.
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In Bayesian statistical inference, the loss function plays an important role, and symmetric loss function, such as the squared error loss L([?
The purpose of the present study is to detect the statistical reasoning errors that academic psychologists make when presented with the results of a statistical inference test.
In the beginning, a brief review of the main contributors to statistical inference, first developed by Sir Ronald Fisher (1890-1962) and after improved in 1933 by Jerzy Neyman and Egon Pearson (2) (to be not confounded with the developer of the well-known Pearson product-moment correlation coefficient, Karl Pearson, who actually was Egon's father), introduces the readers from a historical point of view to what is the principal approach used in rehabilitation research, namely the Neyman-Pearson approach.
1976) make it clear how they see the place of resampling methods in relation to conventional methods of teaching statistical inference.
This paper highlights the role of various forms of assessments of graduate students' attitude towards statistics and conceptions on statistical inference to inform and improve instruction in this area.
This monograph presents a philosophical treatise of various models and interpretations regarding statistical inference based on Bayesian epistemology.
This text for first- and second-year graduate students covers theory and methods of nonparametric statistical inference procedures.
Chapter 9-12 discuss statistical inference, hypothesis testing and ANOVA.
The APA Task Force on Statistical Inference recommended that researchers provide effect-size estimates for primary study outcomes and whenever reporting p values (Wilkinson & the Task Force on Statistical Inference, 1999).
It should be required reading for all statisticians, mathematicians and scientists as it shows how religious beliefs control statistical inference.
There is never any absolute guarantee that a statistical inference will turn out to be correct--just a likelihood.
Quantitative research encompasses a breadth of methodology, including case reports, controlled experiments, quasi-experiments, statistical simulations, surveys, observational studies, and meta-analyses (Wilkinson & the Task Force on Statistical Inference, 1999).

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