# inferential statistics

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Related to Statistical inference: Statistical hypothesis

## in·fer·en·tial sta·tis·tics

statistics from which an inference is made about the nature of a population; the purpose is to generalize about the population, based on data from the sample selected from the population.
References in periodicals archive ?
This textbook for a one-year course in introductory probability and statistical inference assumes no previous background in probability or statistics; however, a previous precalculus course is helpful.
The first English translation of the Portuguese textbook on statistical inference added a chapter on approximation and computationally intensive methods, but retained the goal of integrating the two main schools of statistical thought: the frequentist or classical, and the Bayesian.
Although we are aware of the risks of using arbitrary cut-offs to interpret statistics in the context of statistical inference (see, for example, the discussion about that issue when considering the p-value in Rosnow & Rosenthal, 1989; or how Hoijtink et al., 2016, review this topic for Bayes Factors), we recoded both Bayes factors following the Jeffreys' (1948) labelled intervals.
suggests that the use of statistical inference requires "argument
The first section of the book includes several methodological concepts such as statistical inference and learning, Bayesian philosophy, spatial analysis, reinsurance, and extreme events.
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.
The course includes several themes, notably"the use of statistical sampling" aiming at introducing various means of statistical sampling and statistical inference based on statistical sampling.
Such observational studies infer causality between air quality and mortality through statistical controls, and thus are subject to all the doubts that accompany statistical inference.
It is not possible to gauge the variability of the outcome below the detection limit, but this information is needed to conduct standard statistical inference on the data in the whole range (for example, to estimate the geometric mean of the population from which the sample is drawn).
The use of statistical inference, machine learning and visualization techniques on security data has become a key component of information security strategy.
"We can answer questions about future population growth using standard principles of statistical inference, which has never really been done before."
First, the author reviews the underpinning logic of the common approach to statistical inference, namely the Neyman-Pearson approach, and its main pitfalls.
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