581: Bayesian, Frequentist, and Fiducial Statistics in Data Science
Super Data Science: ML & AI Podcast with Jon Krohn
The Paradox of Data Quantity and Bias Amplification
This chapter explores the challenges of quantifying data quality amidst the abundance of data and how bias can be magnified with larger data sets. It discusses scenarios regarding data quality choices in surveys and the impact on statistical accuracy in political and COVID-19 studies. The chapter also delves into the differences and limitations of frequentist, Bayesian, and fiducial statistics in data science, emphasizing the need to consider all three approaches as complementary rather than superior.
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