The Future of Statistical Modeling

Cadence

Daily

Latest

2026-11-02

By

Andrew Gelman

Based on

12 recent issues

• Covers: Statistical modeling concepts, causal inference, academic research critiques, hypothesis testing methodology, and blog post roundups.
• For: Data scientists, statisticians, academic researchers, and quantitative analysts.
• Best for: Quantitative researchers looking for critical perspectives on statistical modeling and experimental data.
• Skip if: General business leaders looking for non-technical management advice.

Start with

• What hypothesis testing is all about.: Explains core statistical principles and common misconceptions regarding hypothesis testing.
• The problem is noise, not N: why increasing the sample size isn’t all that.: Examines why managing noise matters more than simply expanding sample sizes in research.
• Bayesians are frequentists: Discusses Bayesian workflows, multilevel models, and statistical analysis practices.

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