Bayesian, Frequentist, and Fiducial Statistics in Data Science

Harvard stats prof Xiao-Li MENG founded the trailblazing Harvard Data Science Review. We cover that and why BFFs (Bayesians, frequentists and fiducial statisticians) should be BFFs (best friends forever).

Xiao-Li:
• Is the Founding Editor-in-Chief of the Harvard Data Science Review, a new publication in the vein of the renowned Harvard Business Review.
• Has been a full professor in Harvard’s Dept of Statistics for 20+ years.
• Chaired the Harvard Stats Dept for 7 years.
• Was Dean of Harvard’s Grad School of Arts and Sciences for 5 years.
• Has published 200+ journal articles on statistics, machine learning, and data science, and been cited over 25,000 times.
• Holds a PhD in Statistics from — yep! — Harvard.

Today’s episode will be of interest to anyone who’s keen to better understand the biggest challenges and most fascinating applications of data science today.

In the episode, Xiao-Li details:
• What the Harvard Data Science Review is, why he founded it, and the most popular topics covered by the Review so far.
• The concept of “data minding”.
• Why there’s no “free lunch” with data — tricky trade-offs abound no matter what.
• The surprising paradoxical downside of having lots of data.
• What the Bayesian, Frequentist, and Fiducial schools of statistics are and when each of them is most useful in data science.

The SuperDataScience show's available on all major podcasting platforms, YouTube, and at SuperDataScience.com.