- AKA: Statistical Inference
- It can abide
- See: Statistical Assumption, Statistical Inference, Likelihood Principle, Parameter Estimation, Statistical Hypothesis Testing, Rational Argument, Statistical Default Theory.
- (Wikipedia, 2011) ⇒ http://en.wikipedia.org/wiki/Statistical_inference
- Statistical inference is the process of drawing conclusions from data that are subject to random variation, for example, observational errors or sampling variation. More substantially, the terms statistical inference, statistical induction and inferential statistics are used to describe systems of procedures that can be used to draw conclusions from datasets arising from systems affected by random variation. Initial requirements of such a system of procedures for inference and induction are that the system should produce reasonable answers when applied to well-defined situations and that it should be general enough to be applied across a range of situations. The outcome of statistical inference may be an answer to the question "what should be done next?", where this might be a decision about making further experiments or surveys, or about drawing a conclusion before implementing some organizational or governmental policy.
- statistical inference: The extension of sample results to a larger population. Descriptive statistics (such as the mean or a histogram) provide concise methods for summarizing a lot of information. However, it is inferential statistics that allows one to make statements about the population from a sample. For example, it is often virtually impossible to measure an entire population, but by statistical inference one can use the measured sample statistics to make statements about the unmeasured population (see estimation). However, in order to use the power of statistical inference, certain assumptions about the statistic must first be met. For example, making correct inferences about a population from a sample can often require that random sampling be employed.
- (Abelson, 1995) ⇒ Robert P. Abelson. (1995). “Statistics as Principled Argument.” Psychology Press. ISBN:0805805281
- Cited by ~474 http://scholar.google.com/scholar?cites=2875245395038797672
- QUOTE: To understand the nature of statistical argument, we must consider what types of explanation qualify as answers to why questinos. One characteristics type, the chance explanation, is expressed in statements such as, "These results could easily be due to cahcne," or ...
The strength of a statistical argument is enhanced in accord with the quantitative magnitude of support for its qualitative claim. There are different ways to index magnitude, the most popular of which is the so-called "effect size" (Cohen, 1988; Glass, 1978; Hedges & Olkin, 1985; ...
Our give major criteria for an effective statistical argument depend on the quality of the data and on the skill of the investigator in designing research and presenting results. There are other aspects of statistical arguments that depend hardly at all on data or on skill - instead they ...