The scenario described in the question is a classic example of sampling bias, also known as selection bias123. This type of bias occurs when the sample chosen for a study or survey is not representative of the entire population the study intends to investigate123.
In this case, the healthcare quality professional wants to assess the satisfaction of the community served by the organization, which includes patients living within a 10-mile radius. However, the professional only sends surveys to households within a 3-mile radius. This means that the sample (households within 3 miles) does not accurately represent the entire population (patients within a 10-mile radius). As a result, the findings from this survey may not accurately reflect the satisfaction levels of the entire community served by the organization123.
To avoid this type of bias, it's important to ensure that the sample chosen for a study or survey is as representative as possible of the population being studied123. This might involve using different sampling techniques or adjusting the scope of the survey to ensure a more diverse and representative sample is obtained123.