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

<p>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.</p><p>In this episode you will learn:<br/>• What the Harvard Data Science Review is and why Xiao-Li founded it [5:31]<br/>• The difference between data science and statistics [17:56]<br/>• The concept of &apos;data minding&apos; [22:27]<br/>• The concept of &apos;data confession&apos; [30:31]<br/>• Why there’s no “free lunch” with data, and the tricky trade-offs that abound [35:20]<br/>• The surprising paradoxical downside of having lots of data [43:23]<br/>• What the Bayesian, Frequentist, and Fiducial schools of statistics are, and when each of them is most useful in data science [55:47]</p><p>Additional materials: <a href='https://gate.sc/?url=http%3A%2F%2Fwww.superdatascience.com%2F581&amp;token=b34ca9-1-1654593678017'>www.superdatascience.com/581</a></p>