Sampling bias occurs when a sample is not representative of the population from which it is drawn. From a credit union's point of view, sampling bias can have significant implications, particularly when it comes to decision-making, risk assessment, and understanding member needs. Here are some examples of sampling bias that may arise in a credit union context:

1. Loan Approval Analysis: If a credit union only reviews loans that were approved, and doesn't take into consideration loans that were denied or not applied for, it may not get an accurate understanding of the overall risk profile or the demographics of its borrowers.

2. Member Satisfaction Surveys: If only members who frequently use online banking are surveyed about their satisfaction with the credit union's services, it can lead to an oversight of the needs and opinions of members who prefer in-person banking or don't use digital services.

3. Feedback Collection: If a credit union collects feedback only during annual general meetings, it might miss the opinions of members who cannot attend these meetings, possibly due to work, location, or other commitments.

4. New Service Adoption: A credit union might be interested in the adoption rate of a new digital service. If they only survey younger members, they might get a skewed perspective, as younger individuals are often more tech-savvy than older members.

5. Risk Assessment: When evaluating credit risk, if the sample only includes members from a particular geographical area or profession, the results won't generalize to the entire membership.

6. Product Development: If a credit union is considering launching a new product and only seeks input from long-standing members, it might miss out on the needs and preferences of newer members.

7. Default Rate Estimation: Estimating default rates based only on past economic good times can lead to a misleadingly low default estimate. This can be problematic if the economy turns and more members default on their loans than expected.

8. Demographic Assessments: If the credit union is looking to understand the financial habits of its diverse member base but only samples from a subset (e.g., only urban members, or only members from a certain age group), it may not get a complete picture.

9. Channel Usage: Understanding how members interact with the credit union (e.g., branch visits, online, mobile app) is crucial. If the sample focuses only on urban members, the results might overestimate digital channel usage, as rural members might have different patterns due to limited internet access.

10. Financial Literacy Programs: If the credit union wants to implement financial literacy programs and only surveys members who have availed of large loans in the past, they may not cater to the needs of members who are just starting their financial journey.

To mitigate these biases, credit unions should aim for random sampling where feasible, or at the very least, be aware of the limitations of their sample and be cautious about generalizing their findings.