Final answer:
In the CUSUM model, having a higher threshold T makes the system less sensitive to minor deviations, reduces false alarms, and requires more substantial evidence before signaling a change.
Step-by-step explanation:
The question you've asked pertains to the CUSUM (Cumulative Sum) model, which is a sequential analysis technique used in quality control and other fields to detect shifts in the mean level of a measured variable from a target value. In the context of the CUSUM model, having a higher threshold T affects the sensitivity of the system to changes or shifts. A higher T value means that more evidence (i.e., a larger cumulative sum) is required before the test signals that a change has occurred. Consequently, this makes the system less sensitive to minor deviations but more robust against false alarms, because it would require a more substantial drift in the process before the alert is triggered.
While a higher threshold ensures that only significant changes are detected, it also increases the risk of failing to detect smaller, yet potentially important, shifts quickly. This trade-off between sensitivity and specificity is essential in balancing the timeliness of detection against the likelihood of false positives in monitoring processes.