Editing Statistical inference (section)

{{short description|Process of using data analysis}}
{{distinguish|Statistical interference}}
{{Research}}
'''Statistical inference''' is the process of using [[data analysis]] to infer properties of an underlying [[probability distribution]].<ref name="Oxford">Upton, G., Cook, I. (2008) ''Oxford Dictionary of Statistics'', OUP. {{ISBN|978-0-19-954145-4}}.</ref> '''Inferential statistical analysis''' infers properties of a [[Statistical population|population]], for example by '''testing hypotheses''' and deriving estimates. It is assumed that the observed data set is [[Sampling (statistics)|sampled]] from a larger population.

'''Inferential statistics''' can be contrasted with [[descriptive statistics]]. Descriptive statistics is solely concerned with properties of the observed data, and it does not rest on the assumption that the data come from a larger population. In [[machine learning]], the term ''inference'' is sometimes used instead to mean "make a prediction, by evaluating an already trained model";<ref>{{cite web |url=https://www.tensorflow.org/lite/guide/inference |title=TensorFlow Lite inference |quote=The term ''inference'' refers to the process of executing a TensorFlow Lite model on-device in order to make predictions based on input data.}}</ref> in this context inferring properties of the model is referred to as ''training'' or ''learning'' (rather than ''inference''), and using a model for prediction is referred to as ''inference'' (instead of ''prediction''); see also [[predictive inference]].