Start / Super Data Science: ML & AI Podcast with Jon Krohn / 581 bayesian frequentist and fiducial statistics in data science

Super Data Science: ML & AI Podcast with Jon Krohn

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

85 min • 7 juni 2022

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

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