Statistical inference

Statistical inference is the process of using data analysis to infer properties of an underlying distribution of probability.^[1] Inferential statistical analysis infers properties of a population, for example by testing hypotheses and deriving estimates. It is assumed that the observed data set is 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";^[2] 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.

^ Upton, G., Cook, I. (2008) Oxford Dictionary of Statistics, OUP. ISBN 978-0-19-954145-4.
^ "TensorFlow Lite inference". The term inference refers to the process of executing a TensorFlow Lite model on-device in order to make predictions based on input data.

[Oxford-1] Upton, G., Cook, I. (2008) Oxford Dictionary of Statistics, OUP. ISBN 978-0-19-954145-4.

[2] "TensorFlow Lite inference". The term inference refers to the process of executing a TensorFlow Lite model on-device in order to make predictions based on input data.

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