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Producer's risk is the probability that a good product will be rejected as a bad product by the consumer . When the acceptance reliability level (ARL) is pi0, we can define the producer's risk as: P (Test is Failed|pi0) [1] It calculates the probability of loss from (1) rejecting a batch which, in fact, should have been accepted, or (2 ...
Type I and type II errors. In statistical hypothesis testing, a type I error, or a false positive, is the rejection of the null hypothesis when it is actually true. For example, an innocent person may be convicted. A type II error, or a false negative, is the failure to reject a null hypothesis that is actually false.
Multiple comparisons problem. An example of coincidence produced by data dredging (uncorrected multiple comparisons) showing a correlation between the number of letters in a spelling bee's winning word and the number of people in the United States killed by venomous spiders. Given a large enough pool of variables for the same time period, it is ...
The procedures of Bonferroni and Holm control the FWER under any dependence structure of the p-values (or equivalently the individual test statistics).Essentially, this is achieved by accommodating a `worst-case' dependence structure (which is close to independence for most practical purposes).
The simple Bonferroni correction rejects only null hypotheses with p-value less than or equal to , in order to ensure that the FWER, i.e., the risk of rejecting one or more true null hypotheses (i.e., of committing one or more type I errors) is at most . The cost of this protection against type I errors is an increased risk of failing to reject ...
Illustration of the bullwhip effect: the final customer places an order (whip), which increasingly distorts interpretations of demand as one proceeds upstream along the supply chain. The bullwhip effect is a supply chain phenomenon where orders to suppliers tend to have a larger variability than sales to buyers, which results in an amplified ...
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. [6] The false positive rate depends on the significance level. The specificity of the test is equal to 1 minus the false positive rate.
From the relevant tables it can be established that the type of task in this situation is of the type (F) which is defined as 'Restore or shift a system to original or new state following procedures, with some checking'. This task type has the proposed nominal human unreliability value of 0.003.