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

581: Bayesian, Frequentist, and Fiducial Statistics in Data Science 01:24:30 Share

Super Data Science: ML & AI Podcast with Jon Krohn

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