In hypothesis testing, statistical significance is typically based on calculations involving p-values and Type I error rates. A p-value calculated from a single statistical hypothesis test can be used to determine whether there is statistically significant evidence against the null hypothesis. The upper threshold applied to the p-value in making this determination (often 5% in the scienti c literature) determines the Type I error rate; i.e., the probability of making a Type I error when the null hypothesis is true. Multiple hypothesis testing is concerned with testing several statistical hypotheses simultaneously. Defining statistical significance is a more complex problem in this setting.
CITATION STYLE
Storey, J. D. (2011). False Discovery Rate. In International Encyclopedia of Statistical Science (pp. 504–508). Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-642-04898-2_248
Mendeley helps you to discover research relevant for your work.