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