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However, increased testing also increases the chance of false error reports, and in our experience this brings with it a danger of developing complacency on the part of the tester ('I've seen that fail before, I won't worry about that').
The false error rate was reported as the number of peptide sequences matching the decoy database divided by the total number of non-redundant peptides we obtained across all experiments.
Thus, use of Bonferroni for mitigating false error rates in data sets with large numbers of tests resulted in highly conservative p-values and a very small number of false positives.
This problem has been solved by a new feedback control technique called "dynamic PID control", in which a false error signal proportional to the real error signal is automatically added to the real error signal when A exceeds As (i.e., when the downhill region of the sample is being scanned).
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In the rodent 5-CSRTT, differences between premature responding and false errors (analogous to Go/NoGo or Continuous Performance Task commission errors) have been highlighted (45).
Three types of errors are discussed: false positive errors, false negative errors, and message errors.
There are two types of errors for synchronization code detection, false positive error and false negative error.
First we calculate false positive error, false negative error, and similarity index for each segmentation results of all methods.
False positive error rate/Type I error was 0.05.
Average false positive error, false negative error, and similarity index are, respectively, computed for each compared method based on all segmentation results of all slices.
In summary, false positive error and false negative error are lower; the segmentation result is better.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com