# Statistical Inference

(Redirected from statistical inference)

A Statistical Inference is a data-driven inference in the form of a statistical statement.

**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):****See:**Predicted Number, Causal Inference, Statistical Regression.

## References

### 2017

- 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.

### 2016

- (Wikipedia, 2016) ⇒ http://en.wikipedia.org/wiki/Statistical_inference
**Statistical inference**is the process of deducing properties of an underlying distribution by analysis of data.^{[1]}Inferential statistical analysis infers properties about a population: this includes testing hypotheses and deriving estimates. The population is assumed to be larger than the observed data set; in other words, the observed data is assumed to be 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 does not assume that the data came from a larger population.

### 2002

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