Moreover, the proposed approach employed the sparsity rather than the sparse error to detect the foreground, which has been proven effective and robust to dynamic and corrupted scenes.
The sample size was calculated to be 300 to have an 80% power, considering a 5% type I error to detect an association of a positive test with six variables (patients' characteristics).
With 67 women in the high-FPG group and 50 in the low-FPG group, we had 80% power at a nominal two-sided 1% Type I error to detect unadjusted differences in group means of ∼0.64 SDs.
It is essential, therefore, that the design and routine operations of surveillance activities consider all possible sources of error, attempt to detect such errors and implement strategies to prevent or correct shortcomings.
The aim is to minimise Type II errors (failing to detect a significant difference when one exists), subject to the constraint on Type I errors [ 8].
In general, quality assurance plans should include actions that aim to prevent errors and to detect and reduce those that occur [ 4].
Especially for edging, it's important to keep the look soft and subtle, which will make errors harder to detect.
Although most ERP studies did not report a difference in ERN amplitude for aware and unaware errors [7], [19] [21], these source localisation results are nevertheless in line with some previous reports of reduced ERN amplitudes for undetected errors compared to detected errors [60] or increased ERN amplitudes for aware errors [61].
It is therefore 1 minus the probability of a Type II error (failure to detect a relationship despite the reality of the relationship).
First, the sample size was modest, and may have compromised statistical power, thereby increasing possibility of Type II error (failure to detect actual differences).
