In statistics, statistical inference is the process of drawing conclusions from data that is 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, such as observational errors, random sampling, or random experimentation. 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.
Read more about Statistical Inference: Models/Assumptions, Modes of Inference, Inference Topics
Famous quotes containing the word inference:
“I shouldn’t want you to be surprised, or to draw any particular inference from my making speeches, or not making speeches, out there. I don’t recall any candidate for President that ever injured himself very much by not talking.”
—Calvin Coolidge (1872–1933)