581: Bayesian, Frequentist, and Fiducial Statistics in Data Science

In this episode founding Editor-in-Chief of the Harvard Data Science Review and Professor of Statistics at Harvard University, Prof. Xiao-Li Meng, joins Jon Krohn to dive into data trade-offs that abound, and shares his view on the paradoxical downside of having lots of data.

In this episode you will learn:
• What the Harvard Data Science Review is and why Xiao-Li founded it [5:31]
• The difference between data science and statistics [17:56]
• The concept of 'data minding' [22:27]
• The concept of 'data confession' [30:31]
• Why there’s no “free lunch” with data, and the tricky trade-offs that abound [35:20]
• The surprising paradoxical downside of having lots of data [43:23]
• What the Bayesian, Frequentist, and Fiducial schools of statistics are, and when each of them is most useful in data science [55:47]

Additional materials: www.superdatascience.com/581