While oversampling offers many benefits, it also comes with certain challenges:
1. Resource Intensive: More experiments mean higher consumption of materials, reagents, and time. 2. Data Management: Handling and analyzing large datasets can be complex and requires advanced software and statistical tools. 3. Potential for Data Redundancy: Excessive oversampling can lead to redundant data, which may not contribute additional insights and can complicate analysis.
