Alternative Hypothesis (H1) is a way of referring to the alternative hypothesis in a scientific experiment or business process improvement initiative. While the null hypothesis (H0) in any experiment or research project is that the connection or conclusion suggested by the experiment is false, the alternative hypothesis (H1) is always the assertion that there is a meaningful connection to be investigated. In statistical hypothesis testing, the alternative hypothesis (or maintained hypothesis or research hypothesis) and the null hypothesis are the two rival hypotheses which are compared by a statistical hypothesis test.
In the domain of science, two rival hypotheses can be compared by explanatory power and predictive power. An example might be 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”. 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.
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