Publication

Evidence for Alternative Hypotheses

Stephan Morgenthaler
2013
Book Chapters

Abstract

Most researchers want evidence for the direction of an effect, not evidence against a point null hypothesis. Such evidence is ideally on a scale that is easily in- terpretable, with an accompanying standard error. Further, the evidence from iden- tical experiments should be repeatable, and evidence from independent experiments should be easily combined, such as required in meta-analysis. Such a measure of evidence exists and has been shown to be closely related to the Kullback-Leibler symmetrized distance between null and alternative hypotheses for exponential fam- ilies. Here we provide more examples of the latter phenomenon, for distributions ly- ing outside the class of exponential families, including the non-central chi-squared family with unknown non-centrality parameter.

Official source

https://infoscience.epfl.ch/entities/publication/485fb3c6-634a-4cfa-baa2-8e9c97c5bfc6

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Stephan Morgenthaler
2013
Book Chapters

Abstract

Official source

https://infoscience.epfl.ch/entities/publication/485fb3c6-634a-4cfa-baa2-8e9c97c5bfc6

About this result

Related concepts (32)

Alternative hypothesis

In statistical hypothesis testing, the alternative hypothesis is one of the proposed proposition in the hypothesis test. In general the goal of hypothesis test is to demonstrate that in the given condition, there is sufficient evidence supporting the credibility of alternative hypothesis instead of the exclusive proposition in the test (null hypothesis). It is usually consistent with the research hypothesis because it is constructed from literature review, previous studies, etc.

Null hypothesis

In scientific research, the null hypothesis (often denoted H0) is the claim that no relationship exists between two sets of data or variables being analyzed. The null hypothesis is that any experimentally observed difference is due to chance alone, and an underlying causative relationship does not exist, hence the term "null". In addition to the null hypothesis, an alternative hypothesis is also developed, which claims that a relationship does exist between two variables.

Statistical hypothesis testing

A statistical hypothesis test is a method of statistical inference used to decide whether the data at hand sufficiently support a particular hypothesis. Hypothesis testing allows us to make probabilistic statements about population parameters. While hypothesis testing was popularized early in the 20th century, early forms were used in the 1700s. The first use is credited to John Arbuthnot (1710), followed by Pierre-Simon Laplace (1770s), in analyzing the human sex ratio at birth; see .

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Evidence for Alternative Hypotheses

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