Not to be confused with Statistical interference. Statistical inference is the process of deducing properties of an underlying probability distribution linear statistical inference and its applications by c rao pdf analysis of data.
Inferential statistical analysis infers properties about a population: this includes testing hypotheses and deriving estimates. Inferential statistics can be contrasted with descriptive statistics. Descriptive statistics is solely concerned with properties of the observed data, and does not assume that the data came from a larger population. Statistical inference makes propositions about a population, using data drawn from the population with some form of sampling.
Kitagawa state, “The majority of the problems in statistical inference can be considered to be problems related to statistical modeling”. The conclusion of a statistical inference is a statistical proposition. Any statistical inference requires some assumptions.
A statistical model is a set of assumptions concerning the generation of the observed data and similar data. Descriptions of statistical models usually emphasize the role of population quantities of interest, about which we wish to draw inference.