Control Chart

Correct Answer: Investigate whether there is a special cause operating.

A control chart is used to understand process variation and stability. When seven consecutive points fall on one side of the mean, it's typically a signal that there's a special cause of variation. As a ScrumMaster, the ideal next step would be to investigate this anomaly. This special cause could be any factor that is not part of the regular, random variation of the process and it could significantly impact the performance. The other options which suggest increasing the control limits, ignoring the problem, and adjusting the mean do not actually address the root cause and may result in ignoring or hiding critical issues and leading to false results.

Correct Answer: There might be a special cause variation and I should investigate to see what might have caused these outliers.

The best interpretation when points fall outside the upper control limit in a Scrum team's control chart is that there might be a special-cause variation. This indicates that something unusual has occurred which is not part of the normal process and could disrupt the stability of the project. It's crucial for the Scrum Master to investigate these outliers to identify and address the cause. This could involve assessing any changes to the team's working methods, environmental factors, or unexpected obstacles encountered by the teamAs for the other options, here's why they are incorrect:- 'The team is performing better than expected and I don't need to do anything': This response is incorrect because a point above the upper control limit doesn't necessarily show better performance. Rather, it signals inconsistency that needs to be investigated- 'I should ignore this information as it's probably a mistake': It's unwise to ignore data points that fall outside of the control limits. Ignoring these signals can lead to bigger problems or missed opportunities for improvements- 'Our control limits are incorrectly set and I should adjust them immediately': Simply adjusting the control limits whenever data falls outside them defeats the purpose of having limits. The control limits should be based on the process capability and should not be arbitrarily adjusted each time the data falls outside the limits.

Correct Answer: More chances of false alarms indicating a problem when there isn't one.

In a control chart, the upper and lower control limits are set to define the range of acceptable variation in a process. If these limits are too close together, it can result in an increased number of false alarms. This happens because even minor variations in the process, which are normal and not indicative of a problem, will appear to fall outside the control limits, thus triggering a false alarm. This can cause unnecessary corrective action, adding cost and potentially disrupting a normally functioning process. The other answers could be misleading. While the control chart's accuracy is not directly impacted by the closeness of control limits, interpretation might be difficult, leading to false interpretations (Answer 159571). The process mean would not shift because of close control limits; rather, it's the sensitivity to variations that increases (Answer 159573). Identifying special cause variations would not be directly impacted, but there might be an overreaction to common cause variations that are falsely determined as special cause variations (Answer 159576).