Good videos, with context.

A convincing result needs a reproducible path

Data science & statistics videos

Data science videos are most useful when they help you distinguish a striking result from a trustworthy analysis. Begin with how findings can mislead, then examine the workflow that produced and communicates them.

Research claims and notebook state

The research-reliability explainer is a general entry into why a published claim may deserve further checking. Grus’s notebook critique is aimed more directly at programmers who use interactive analysis. Treat the pair as questions about evidence and reproducibility, not a ban on a particular tool. Try rerunning a small analysis from a clean process with its data and assumptions recorded.

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Questions to take into the video

A critique of research practice does not mean every paper is wrong, and a critique of notebooks does not make scripts automatically reproducible.

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Probability & game theory ↗Data visualization & information design ↗
Sources and selection notes

By . Updated . We compared source-video titles, available uploader descriptions and the linked archive records. The choice of starting points and viewing questions is our editorial guidance. These notes do not claim full-video viewing or independent expert review.

The complete list below preserves the October 31, 2026 snapshot and its original score order. Its labels and summaries include AI-generated source material; topic membership is broader than our selected starting points. Availability and factual claims can change. Suggest a correction.

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