Two-sample hypothesis testing

Two-sample hypothesis testing is a statistical method that involves comparing two samples to determine if they are from the same population. The null hypothesis assumes that the samples are from the same distribution, while the alternative hypothesis assumes that there is a difference between the two samples. This method is used to test for changes or differences between two groups or populations.From: Measurement and Data Analysis for Engineering and Science [2017], Continuous Authentications Using Frequent English Terms [2018]

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Published in Patrick F. Dunn, Michael P. Davis, Measurement and Data Analysis for Engineering and Science, 2017

Patrick F. Dunn, Michael P. Davis

Statistical tools also are used in hypothesis testing (17.8) and typically applied to either one sample (17.8.1) or two samples (17.8.2). One-sample hypothesis testing is used to assess the reliability of a claim, for example, that a pressure transducer is reliable. Two-sample hypothesis testing compares one sample with another to ascertain whether or not they come from the same population. Other statistical methods are used in the design of experiments (17.9) and in assessing the extent to which each factor contributes to an experimental result (17.10).

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Published in Applied Artificial Intelligence, 2018

Alaa Darabseh, Sima Siami-Namini, Akbar Siami Namin

The initial work was introduced by Hawkins, Qiu, and Kang (2003) and concentrated on detecting changes in mean shift or variance of normal random distribution variables. Hawkins et al. assumed that no prior information is available regarding distributions parameters. Hawkins et al. developed an approach based on the logic of two-sample hypothesis testing, where the null hypothesis assumes that all observations in the sequence come from the same distribution and no change point occurs, against the alternative hypothesis, which assumes that there is a change point in the sequence of observations that split them into two sections. This work has been expanded by other researchers to detect more complicated changes, including those assuming that the ongoing data observations distribution is unknown (Hawkins and Deng 2010; Ross and Adams 2011; Zou and Tsung 2010).

Two-sample hypothesis testing

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