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The scope of test cases usually rely on the software tester involved, who uses experience and intuition to determine what situations commonly cause software failure, or may cause errors to appear. [2] Typical errors include divide by zero, null pointers, or invalid parameters. [3]
The false positive rate (FPR) is the proportion of all negatives that still yield positive test outcomes, i.e., the conditional probability of a positive test result given an event that was not present. The false positive rate is equal to the significance level. The specificity of the test is equal to 1 minus the false positive rate.
A checksum of a message is a modular arithmetic sum of message code words of a fixed word length (e.g., byte values). The sum may be negated by means of a ones'-complement operation prior to transmission to detect unintentional all-zero messages.
For a Type I error, it is shown as α (alpha) and is known as the size of the test and is 1 minus the specificity of the test. This quantity is sometimes referred to as the confidence of the test, or the level of significance (LOS) of the test. For a Type II error, it is shown as β (beta) and is 1 minus the power or 1 minus the sensitivity of ...
Type II errors can be thought of as errors of omission, in which a misleading status quo is allowed to remain due a failures in identifying it as such. For example, if the assumption that people are innocent until proven guilty were taken as a null hypothesis, then proving an innocent person as guilty would constitute a Type I error, while ...
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In statistical hypothesis testing, there are various notions of so-called type III errors (or errors of the third kind), and sometimes type IV errors or higher, by analogy with the type I and type II errors of Jerzy Neyman and Egon Pearson. Fundamentally, type III errors occur when researchers provide the right answer to the wrong question, i.e ...
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