Abstract
Hypothesis tests, such as the t-test, chi-square test, and ANOVA test, yield p-values that represent the probability of observing a particular study result, or a more extreme result, if a pre-specified null hypothesis about the population were true. A p-value threshold, such as 0.05, is typically used to declare statistical significance. Consequently, a hypothesis test may declare a result to be significant when in fact there is no actual difference in the population (type I error) or declare a result to be nonsignificant when in fact there is an actual difference in the population (type II error). Study power, which is the probability of not making a type II error, is influenced by sample size, effect size, variation, and the threshold value for declaring significance.
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsAuthor information
Authors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this chapter
Cite this chapter
Kestenbaum, B. (2019). Hypothesis Tests in Practice. In: Epidemiology and Biostatistics. Springer, Cham. https://doi.org/10.1007/978-3-319-97433-0_14
Download citation
DOI: https://doi.org/10.1007/978-3-319-97433-0_14
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-97432-3
Online ISBN: 978-3-319-97433-0
eBook Packages: MedicineMedicine (R0)