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The false positive rate is equal to the significance level. The specificity of the test is equal to 1 minus the false positive rate. In statistical hypothesis testing, this fraction is given the Greek letter α, and 1 − α is defined as the specificity of the test. Increasing the specificity of the test lowers the probability of type I errors ...
In statistics, the false discovery rate (FDR) is a method of conceptualizing the rate of type I errors in null hypothesis testing when conducting multiple comparisons. FDR-controlling procedures are designed to control the FDR, which is the expected proportion of "discoveries" (rejected null hypotheses ) that are false (incorrect rejections of ...
The false positive rate is calculated as the ratio between the number of negative events wrongly categorized as positive (false positives) and the total number of actual negative events (regardless of classification). The false positive rate (or "false alarm rate") usually refers to the expectancy of the false positive ratio.
An estimate of d′ can be also found from measurements of the hit rate and false-alarm rate. It is calculated as: d′ = Z(hit rate) − Z(false alarm rate), [15] where function Z(p), p ∈ [0, 1], is the inverse of the cumulative Gaussian distribution. d′ is a dimensionless statistic. A higher d′ indicates that the signal can be more ...
However, in most fielded systems, unwanted clutter and interference sources mean that the noise level changes both spatially and temporally. In this case, a changing threshold can be used, where the threshold level is raised and lowered to maintain a constant probability of false alarm. This is known as constant false alarm rate (CFAR) detection.
In statistical quality control, the CUSUM (or cumulative sum control chart) is a sequential analysis technique developed by E. S. Page of the University of Cambridge. It is typically used for monitoring change detection. [1] CUSUM was announced in Biometrika, in 1954, a few years after the publication of Wald's sequential probability ratio test ...
V is the number of false positives (Type I error) (also called "false discoveries") S is the number of true positives (also called "true discoveries") T is the number of false negatives (Type II error) U is the number of true negatives = + is the number of rejected null hypotheses (also called "discoveries", either true or false)
In typical use, it is a function of the test used (including the desired level of statistical significance), the assumed distribution of the test (for example, the degree of variability, and sample size), and the effect size of interest. High statistical power is related to low variability, large sample sizes, large effects being looked for ...