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.
Copyright © 2012, Campbell R. Harvey. All Rights Reserved.

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.
Collins Dictionary of Business, 3rd ed. © 2002, 2005 C Pass, B Lowes, A Pendleton, L Chadwick, D O’Reilly and M Afferson
References in periodicals archive ?
If Bayesian alternatives to classical p-values are useful and more accurate to make decisions about null hypothesis, experimental and observational researchers would benefit from them when making statistical inferences in their research.
Most of the literature about the process ability index of statistical inference research assumes that the quality characteristics of the product obey a normal distribution.
Conventional statistical inferences (such as standard error, f-tests, etc.) depend on the assumption of random sampling.
ERIC Descriptors: Research Methodology; Simulation; Bias; Statistical Inference; Research Design; Models; Regression (Statistics); Equations (Mathematics); Hierarchical Linear Modeling
In this article, informal statistical inference is introduced as an approach to teaching statistics.
Theorem 4 can in turn be used to establish statistical inferences for testing marginal changes in population quantile shares and quantile means (Zheng 1996).(3) Having derived various asymptotic distributions of marginal changes, we can perform conventional statistical inferences to test marginal rank dominances.
In experiment D, we know exactly what population was sampled and hence we may wish to make statistical inferences about its population parameters.
Peter Urbach has argued that randomized experiments serve no useful purpose in testing causal hypotheses (Urbach [1985], Howson and Urbach [1989]).(1) In this paper I shall show that he misunderstands the role of randomization in this context, as a result of failing to separate issues of statistical inference sufficiently clearly from problems about identifying causes.
The availability of such "artificial" samples permits students -- as well as researchers -- to experiment systematically with data in order to estimate probabilities and make statistical inferences.
Determining the effectiveness of interventions is one of the functions of health services research." To date very little money has been spent on health services research.12 Moreover, according to one evaluation, despite researchers' best intentions, most studies are invalid or scientifically inadequate in terms of their design, data, statistical inferences, or documentation.13 Consequently, we know scientifically little or nothing about the effectiveness of most interventions.
Statistical inferences formed through the analysis of probability samples are preferred because inferences drawn from nonprobability samples may be unreliable and inaccurate.
Then he explains the mechanics and dynamics of the system, rules of evidence as they apply to an expert witness, the appointment of experts and the written report, the expert forensic scientists in court, statistics and statistical inferences, and ethical and scientific considerations.

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