other · 2020
Statistical Hypothesis Testing
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Abstract
A statistical hypothesis is a hypothesis that is testable, on the basis of observing a process modeled by a set of random variables that follow a probability distribution known a priori. Hypothesis testing (or confirmatory data analysis) is a method of statistical inference that resorts to tests of significance to determine the probability that a statement is true, and at what likelihood such a statement may be accepted as true. This chapter explains basic process of hypothesis testing that consists of four sequential steps: formulation of the null hypothesis, H 0; identification of an appropriate test statistic that can be used to assess the truth of the null hypothesis (dependent on the nature of the data and of the test); computation of the P-value, or associated probability that a test statistic at least as significant as the one determined from the sample data would be obtained; and comparison of the P-value with an acceptable significance level.
Cite this paper
Malcata, F. X. (2020). Statistical Hypothesis Testing. https://doi.org/10.1002/9781119490319.ch19
Malcata, F. Xavier. “Statistical Hypothesis Testing.” 2020, pp. 919–26, https://doi.org/10.1002/9781119490319.ch19.
Malcata, F. Xavier. 2020. “Statistical Hypothesis Testing.” https://doi.org/10.1002/9781119490319.ch19.
Malcata, F.X. (2020) ‘Statistical Hypothesis Testing’. Available at: https://doi.org/10.1002/9781119490319.ch19.
@misc{malcata2020statistical,
title = {{Statistical Hypothesis Testing}},
author = {Malcata, F. Xavier},
pages = {919--926},
year = {2020},
doi = {10.1002/9781119490319.ch19}
}Made from the index record. Check names, capitalisation and page numbers against the paper. Need another style? AI for Research has over 10,000.