Experimental Design for Laboratory Biologists: Maximising Information and Improving Reproducibility

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This practical guide shows biologists how to design reproducible experiments that have low bias, high precision, and results that are widely applicable. With specific examples using both cell cultures and model organisms, it shows how to plan a successful experiment. It demonstrates how to control biological and technical factors that can introduce bias or add noise, and covers rarely discussed topics such as graphical data exploration, choosing outcome variables, data quality control checks, and data pre-processing. It also shows how to use R for analysis, and is designed for those with no prior experience. This is an ideal guide for anyone conducting lab-based biological research.

Brief table of contents

  1. Introduction
  2. Key ideas in experimental design
  3. Replication (what is N?)
  4. Analysis of common designs
  5. Planning for success
  6. Exploratory data analysis
  7. Appendix A. Introduction to R
  8. Appendix B. Glossary


This is a wonderfully lucid introduction to experimental design, written by an author who is clearly aware of the pitfalls that exist for the unwary experimenter. Highly recommended.
– Prof. Dorothy Bishop, University of Oxford

It is really refreshing to come across a book that explicitly deals with experimental design and analysis. This new book clearly lays out what can and should be done and is written by an acknowledged expert and I have no doubt that this book will become a recommended read for all those contemplating undertaking work of this type.
– Prof. Roger Barker, University of Cambridge

This important textbook is a timely and highly useful contribution in the pressing quest to improve the robustness, rigor, and reproducibility of current biological and preclinical research. This volume is unique... [as] it is immensely readable and accessible even for those with little previous knowledge, in combining all relevant aspects in a practical, concise and comprehensive manner, and in its clear focus on factors that help to improve the quality of research.
– Prof. Ulrich Dirnagl, Charite University Hospital, Germany

And from Twitter...

Must read for all cell biologists https://t.co/GA6tyBvCFe
Stanley Lazic's book on experimental design is also highly recommended.

— Steve Royle (@clathrin) April 6, 2018

Yes, I have learned a lot about experimental design in the last year or so. I found @StanLazic’s book a great resource:

Experimental Design for Laboratory Biologists: https://t.co/CaokGgsZ14

Also: Modern Statistics for the Life Sciences by Grafeb & Hails.

— Sam Lord / Samuel J. Lord (@samjlord) March 10, 2021

The Productive Researcher by @profmarkreed, HHMI Making the Right Moves (https://t.co/IcQZ3Y75VY), Experimental Design for Laboratory Biologists by Stanley Lazic (https://t.co/l0vaGkOqkH), Statistics Explained: An Introductory Guide for Life Scientists by Steve McKillup.

— IF (@IF91) 31 May 2018

Good question. I was encouraged to use it by @StanLazic's excellent book on experimental design. I tried looking for examples in neuroscience papers. There were a few examples but ANOVA is no doubt dominant.https://t.co/G8tSP1Gcnl

— Alex Chamessian (@achamess) April 9, 2020

Tomorrow I will be talking about #reproducibility, #replicability, #ExperimentalDesign and 'what is N?' @lab_becker departmental seminar.

Thanks to @StanLazic for his fantastic book!

Link to code and pdf:https://t.co/hWLWM4nlUt pic.twitter.com/PjVQFonEMu

— Tamas Schauer (@tamas_schauer) April 14, 2020

I love your book by the way. It's helped me loads moving from epi to lab stats, and have recommended it to many at my institute.

— George Savva (@georgemsavva) January 30, 2020

studying Stanley Lazic's (@AstraZeneca's stats guru) meditative (=detailed) book on Experimental Design https://t.co/T1wzv97Z76 think P(Rash|Lupus)=high, P(Lupus|Rash)=low 'inversion between what a p-value tells u & what u want to know is main reason for its misinterpretation' https://t.co/JILwuR5MPU

— attilacsordas (@attilacsordas) February 11, 2019