Personal notes on hypothesis testing in statistics.

Based on notes taken during a course [1].

## Hypotheses

- null hypothesis
- Hypothesis assumed to be true. Often denoted by \(H_0\).
- alternative hypothesis
- Hypothesis for which we are looking for evidence. Often denoted by \(H_1\) or \(H_a\).
- simple hypothesis
- Hypothesis which completely specifies the distribution.
- composite hypothesis
- Hypothesis which is not simple. I.e., aspects of the distribution are left unspecified.

Hypothesis testing either rejects or fails to reject the null hypothesis.

## Types of error

- Type I error
- Rejecting the null hypothesis when it is true.
- Type II error
- Failing to reject the null hypothesis when it is false.

## See also

## References

[1]

Jem Corcoran. Statistical inference and hypothesis testing in data science applications. Course on Coursera.