Balancing Quantity and Quality: Sampling Challenges in Big Data

This talk builds upon my previous discussion on what is big in big data from a sampling statistician's perspective. Using the same lens, it delves deeper into Big Data quality issues, emphasizing that Big Data is not merely a matter of size. The common misconception that 'bigger is always better' oversimplifies the complex, multilayered, and multipurpose nature of big data, and disregards the fundamental statistical principle that large sample sizes alone cannot correct selection bias and poor data quality. With the rise of new digital data sources, achieving a balance between quality and quantity becomes an even more crucial imperative. We will explore recent literature grounded in the power of sampling methodology, that offers a fresh, practical perspective on how to strike this balance, essential to fully harness the opportunities around Big Data.