# Statistics - Statistic

In general, each statistic is an estimate of a parameter, whose value is not known exactly. Every number found using a sample is just an (approximation|estimation) of a parameter (the truth).

• in descriptive statistics, a descriptive statistic is used to describe the data;
• in estimation theory, an estimator is used to estimate a parameter of the distribution (population);
• in statistical hypothesis testing, a test statistic is used to test a hypothesis.

However, a single statistic can be used for multiple purposes – for example the sample mean can be used to describe a data set, to estimate the population mean, or to test a hypothesis.

A statistic is a descriptive number::

Example:

A statistic, when used to estimate a population parameter, is called an estimator. For instance, the sample mean is a statistic that estimates the population mean, which is a parameter.

Example:

Sampling error implies that they will vary from one study to the next (from one data set to another).

A statistic describes a sample whereas a population parameter describes the population. A statistic is an observable random variable, which differentiates it both from a population parameter that is a generally unobservable quantity.

A statistic may not be representative for every individuals in the sample

The number of captured individuals is a statistic as it deals with the sample. The actual population is a parameter that we are trying to estimate.

## Parametric

A statistic is called parametric when we are trying to make inferences about population parameters, based on a sample.

## Documentation / Reference

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