Statistics for Public Policy: A Practical Guide to Being Mostly Right

by Jeremy G. Weber

A long-overdue guide on how to use statistics to bring clarity, not confusion, to policy work.


Statistics are an essential tool for making, evaluating, and improving public policy. Statistics for Public Policy is a crash course in wielding these unruly tools to bring maximum clarity to policy work. Former White House economist Jeremy G. Weber offers an accessible voice of experience for the challenges of this work, focusing on seven core  



Thinking big-picture about the role of data in decisions
Critically engaging with data by focusing on its origins, purpose, and generalizability
Understanding the strengths and limits of the simple statistics that dominate most policy discussions
Developing reasons for considering a number to be practically small or large  
Distinguishing correlation from causation and minor causes from major causes
Communicating statistics so that they are seen, understood, and believed
Maintaining credibility by being right (or at least respectably wrong) in every setting

Statistics for Public Policy dispenses with the opacity and technical language that have long made this space impenetrable; instead, Weber offers an essential resource for all students and professionals working at the intersections of data and policy interventions. This book is all signal, no noise.

Genres: Politics

195 pages, Kindle Edition

First published January 16, 2024

Reviews

[Sandeep Nair · Rating 5 out of 5]

This is one of the best practical statistics books I have read. It covers the intuition behind statistical methods and focuses on how to use statistics effectively for policy through good journalism.

As a data scientist familiar with all the concepts covered, I found the tight and lucid writing enjoyable and not tiring.

The book emphasizes communicating statistics to policymakers who may not have the time, attention span, patience, or aptitude for complicated or poorly presented numbers. I have experienced every communication shortcoming the author mentions and felt validated by the corrective suggestions he makes.

A must-read for any analyst, scientist, journalist, or policy wonk.

[Matouš Fiala · Rating 5 out of 5]

A practical handbook for anyone who wants to use statistics without embarrassing themselves. My biggest takeaways, in short:

- Know your sample well: who's in it, what you need it for, and how much you can generalise it. Just because it's not a random sample doesn't mean it's not useful.

- Know your data well: definitions, units, quality, distribution, missing values. What does a one-unit increase in poverty mean? Is it a lot? Which definition of poverty does it follow?

- Don't underestimate simple statistics - know precisely what they're saying and what they're for

- It's both important and difficult to determine if an effect is large or small, important or worth ignoring. This won't come from statistical significance but from the context and your audience.

- Just because there is a true causal effect doesn't mean it explains all of the phenomenon. People stay home when it's cold, but that doesn't mean people stayed home in 2020 because it was cold. Sanity check yourself when talking about causality.

- Know your audience and present your findings accordingly - for laypeople: simplify, simplify, simplify.

- Be kind to future you and your fact-checker and document your analysis well

[Jack Maguire · Rating 5 out of 5]

This book delivers exactly what the author intended: a practical guide on applying statistics in real-world environments. Though primarily focused on public policy, it’s equally valuable for professionals in any data-driven field, including marketing.

At its core, the book emphasizes starting with simple statistics, like understanding data distribution and relationships between segments. This approach helps readers grasp the fundamentals before diving into deeper analysis. One of its most important contributions is clarifying the correlation vs. causality debate, urging thoughtful consideration of how statistics relate to real-world outcomes.

The book is particularly effective in its guidance on presenting data. It stresses the need to tailor information to your audience: high-level decision-makers require broad, actionable insights, while analysts may need more detailed data. Practical tips on building tables and graphs ensure you communicate statistical findings efficiently.

Although it’s not a thrilling read, the book’s value lies in its practicality. It bridges the gap between academic statistics and real-world decision-making, making it a must-read for anyone looking to use data more effectively in their work or daily life.

[Alex Goodwin · Rating 5 out of 5]

This is an excellent review of statistics that will help you think more critically. I really appreciate the book focusing on the fundamentals. I have taken two stats courses between college and grad school and I wish I had read this book before then. I have a stronger grasp of concepts like standard error. It's not just some number I calculate to get my homework done, but a concept I understand.

The book also goes into other issues like calculating statistics without taking them into context. You need to know not just whether or not two things correlate, but the magnitude of the effect. Calculating a p-value doesn't get you very far in many cases and yet it forms the basis of many academic papers (including my thesis, whoops).

There is also a chapter where he goes into how to effectively communicate statistics to different audiences. That's definitely something that I could have used when I was younger and is essential for anyone who uses statistics for a living.

[Jacob · Rating 5 out of 5]

This books does a great job in what it sets out to achieve. Many statistics students know about t-tests, random samples, regressions, and p-values, but understanding how to apply these within the constraints of policy-making is less clear. There are always tradeoffs, competing interests, battles for funding and attention etc., and being effective requires more than a grasp of the math. I particularly liked his recommendation that statisticians should not be afraid to make claims about magnitude, e.g., “this is a meaningful difference in outcomes between these two groups”, rather than only stating the numbers and being afraid to draw any conclusions. I learned a lot and am glad I picked this one up!