Alternative hypothesis

In statistical hypothesis testing, the alternative hypothesis is a position that states something is happening, a new theory is preferred instead of an old one (null hypothesis).[1] It is usually consistent with the research hypothesis because it is constructed from literature review, previous studies, etc. However, the research hypothesis is sometimes consistent with the null hypothesis.

Main article: Statistical hypothesis testing

In statistics, alternative hypothesis is often denoted as H_a or H₁. Hypotheses are formulated to compare in a statistical hypothesis test.

In the domain of inferential statistics two rival hypotheses can be compared by explanatory power and predictive power.

Example

An example is where water quality in a stream has been observed over many years, and a test is made of the null hypothesis that "there is no change in quality between the first and second halves of the data", against the alternative hypothesis that "the quality is poorer in the second half of the record".

History

The concept of an alternative hypothesis in testing was devised by Jerzy Neyman and Egon Pearson, and it is used in the Neyman–Pearson lemma. It forms a major component in modern statistical hypothesis testing. However it was not part of Ronald Fisher's formulation of statistical hypothesis testing, and he opposed its use.[2] In Fisher's approach to testing, the central idea is to assess whether the observed dataset could have resulted from chance if the null hypothesis were assumed to hold, notionally without preconceptions about what other models might hold. Modern statistical hypothesis testing accommodates this type of test since the alternative hypothesis can be just the negation of the null hypothesis.

Types

In the case of a scalar parameter, there are four principal types of alternative hypothesis:

• Point. Point alternative hypotheses occur when the hypothesis test is framed so that the population distribution under the alternative hypothesis is a fully defined distribution, with no unknown parameters; such hypotheses are usually of no practical interest but are fundamental to theoretical considerations of statistical inference and are the basis of the Neyman–Pearson lemma.
• One-tailed directional. A one-tailed directional alternative hypothesis is concerned with the region of rejection for only one tail of the sampling distribution.
• Two-tailed directional. A two-tailed directional alternative hypothesis is concerned with both regions of rejection of the sampling distribution.
• Non-directional. A non-directional alternative hypothesis is not concerned with either region of rejection, but, rather, it is only concerned that null hypothesis is not true.

References

1. Carlos Cortinhas; Ken Black (23 September 2014). Statistics for Business and Economics. Wiley. p. 314. ISBN 978-1-119-94335-8.
2. Cohen, J. (1990). "Things I have learned (so far)". American Psychologist. 45 (12): 1304–1312. doi:10.1037/0003-066X.45.12.1304. S2CID 7180431.

See also

• Null hypothesis
• Type I and type II errors