Which method assumes that measurements before the cause of the problem will be normal and measurements after will not be normal?

The method that assumes measurements taken before the cause of the problem will be normal, while measurements taken afterward will not be normal, is known as the Half-split method. This approach is effective in analyzing situations where a change or a fault is suspected to have occurred midway through a process or measurement sequence.

In this method, the analysis leverages the idea that the conditions or parameters of interest will demonstrate a change, allowing for a clearer identification of the impact caused by the issue. By splitting the measurements into two segments, the method allows for a focused assessment on how things have changed, particularly in scenarios where the transition from a normal to a non-normal state signifies that an incident or malfunction is present.

In contrast, other methods mentioned do not emphasize the temporal aspect of measurements relative to when an anomaly occurs or how that sequencing can delineate normal from abnormal behavior. For instance, the Full-split, Time-split, and Equal-split methods typically deal with measurement analysis but do not specifically align with the idea of assessing the state of measurements based on a causal event's timing. Thus, the Half-split approach stands out in its focus on timing and change.