Statistical Inference

Context:
- It can be produced by a Statistical Inference System (solving a statistical inference task).
- It involves Statistical Hypothesis Testing and Statistical Modelling.
Example(s):
Counter-Example(s):
- a Data-Driven Heuristic Inference.
- a Logical Inference.
- a Descriptive Statistic, such as central tendency.
See: Inductive Inference, Predicted Number, Causal Inference, Statistical Regression, Descriptive Statistics, Data Analysis, Probability Distribution, Statistical Population, Sampling (Statistics).

References

(Wikipedia, 2019) ⇒ https://en.wikipedia.org/wiki/statistical_inference Retrieved:2019-6-14.
- Statistical inference is the process of using data analysis to deduce properties of an underlying probability distribution. 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.

Digio (https://stats.stackexchange.com/users/83065/digio), Differences between logistic regression and perceptrons, URL (version: 2017-06-07): https://stats.stackexchange.com/q/284013
- QUOTE: ... Long story short, logistic regression is a GLM which can perform prediction and inference, whereas the linear Perceptron can only achieve prediction (in which case it will perform the same as logistic regression). The difference between the two is also the fundamental difference between statistical modelling and machine learning.

(Garthwaite et al., 2002) ⇒ Paul Garthwaite, Ian Jolliffe, Byron Jones. (2002). “Statistical Inference, 2nd edition." Oxford University Press. ISBN:0-19-857226-3
- QUOTE: Adopting a broad view of statistical inference, the text concentrates on what various techniques do, with mathematical proof kept to a minimum. The approach is rigorous but accessible to final year undergraduates. Classical approaches to point estimation, hypothesis testing and interval estimation are all covered thoroughly with recent developments outlined.

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