Beta Risk measures the chance of making a Type II error in data interpretation, the opposite of making the Type I error measured by Alpha Risk. Beta Risk describes the level of risk a project team has when they conclude that something is not significant, when in fact, it really is significant.
This can occur when a team analyzes the outcomes of a current process (Process A) with a new approach (Process B). The new approach may, for example, lower the number of product defects. But it might not seem a significant enough change for the team. By failing to recognize the significance of differences in statistical data, teams made a Type II error.
Beta Risk and Type II Errors in the Workplace
In the context of business operations, the outcomes of a Type II error depend on the nature of the task and the industry. A simple example is in the automotive industry, when project teams looking at the details of an operation dismiss a statistical detail as insignificant. This can lead to a defective part that the company must recall. Teams sometimes refer to Beta Risk as Consumer Risk.
On project teams, Beta Risk can occur when teams use a T-test to determine whether or not a particular method is better than their current method. If the conclusion is that both methods are virtually the same (not significantly different from each other) and that the new method under testing makes no significant difference, when in fact it really does make a significant difference, the team just made a Type II error. This is the opposite of Alpha Risk which is when something is considered significant when in fact it really is not significant.
It’s important to understand the level of Beta Risk for a Type II error. An acceptable level of Beta Risk is typically 10%. Anything above should automatically result in gathering a larger data set and revisiting the issue. The larger the dataset set, the lower the Beta Risk and a chance for a Type II error.
