I gradually shifted my statistics teaching from significance testing — traditional but bamboozling — to estimation (confidence intervals), which I called "the new statistics" because, although not new, relying on it would, for many researchers, be very new. It’s more informative, makes sense, and is a pleasure to teach and use. I "retired" to write Understanding the New Statistics. Then Open Science arrived—hooray! Robert Calin-Jageman joined me for an intro textbook with Open Science and The New Statistics all through. Our first edition came out in 2017. The second edition has wonderful new open-source software (‘esci’), which is also ideal for more advanced students and researchers. Enjoy!
I wrote
Introduction to the New Statistics: Estimation, Open Science, and Beyond
Charlotte Pennington eagerly started her PhD but became anxious and depressed when her experiments "didn’t work’" and were refused publication. She read about the replication crisis — many published studies won’t replicate—and Open Science. Soon, it was her passion to teach about Open Science and what’s needed to achieve efficient research and trustworthy research literature.
The result is A Student’s Guide to Open Science. It’s short, clear, and highly readable. She’s well-informed and covers the story in engaging detail. There are practical guides to using Open Science practices and numerous links to follow as you wish. Statistical issues are mentioned but are not her focus.
"Dr Charlotte R. Pennington has pulled off a remarkable trifecta of being clear, concise, and comprehensive in covering the origins of the open science movement and practical advice for adopting the behaviors"
Professor Brian Nosek, Executive Director, Center for Open Science; University of Virginia, US
"My hope is that every psychology student will finish their degree with a heavily annotated, well-thumbed copy of this important and timely book!"
Dr Madeleine Pownall, University of Leeds, UK
"This book should be on the reading list for all university science degrees and on all library bookshelves. It is concise, accessible, and remarkably interactive,…
I love how Ritchie starts with a rather wry account, addressed directly to you as a would-be researcher, of the challenges and weirdness you’ll encounter as you launch into your own research.
His own research helped sparked recognition of the ‘replication crisis,’ so he’s well placed to tell us about misguided practices and the sometimes wicked deeds of researchers. More happily, he describes how we can return to the sound foundations of good scientific practice. A current term for that is ‘Open Science.’
An insider’s view of science reveals why many scientific results cannot be relied upon – and how the system can be reformed.
Science is how we understand the world. Yet failures in peer review and mistakes in statistics have rendered a shocking number of scientific studies useless – or, worse, badly misleading. Such errors have distorted our knowledge in fields as wide-ranging as medicine, physics, nutrition, education, genetics, economics, and the search for extraterrestrial life. As Science Fictions makes clear, the current system of research funding and publication not only fails to safeguard us from blunders but actively encourages bad…
You may have heard of ‘significance testing,’ and the magical ‘p < .05,’ which somehow makes a research result ‘significant,’ which is often taken as (almost) ‘true.’ Even if you haven’t heard of all that, Kline explains clearly why significance testing has been disastrous for science, leading to misleading conclusions and much valuable research not even being reported.
He draws on my work to explain how ‘the new statistics’ (estimation) is a much better way to understand results. The first chapter is fairly easy to read. Later chapters are also terrific but get more technical as Kline explains lots of ways to do things better. As I’m quoted on the back cover, “Read this book and see the future!” Happily, the future is increasingly looking as Kline recommended.
Traditional education in statistics that emphasises significance testing leaves researchers and students ill prepared to understand what their results really mean. Specifically, most researchers and students who do not have strong quantitative backgrounds have difficulty understanding outcomes of statistical tests.
As more and more people become aware of this problem, the emphasis on statistical significance in the reporting of results is declining. Increasingly, researchers are expected to describe the magnitudes and precisions of their findings and also their practical, theoretical, or clinical significance.
This accessibly written book reviews the controversy about significance testing, which has now crossed various disciplines as…
Yes, this is a textbook, but if you are seeking a research design and methods text for psychology or a related discipline, this is easily my top choice.
There are lots of references to topical stories to keep everything relevant for students. There’s a truckload of valuable stuff online to support both teachers and learners. This fourth edition is right up-to-the-moment, Chapter 3 especially so, as it explains three types of scientific claims, and four types of validity that researchers should aim to achieve. That may sound forbidding, but Morling’s examples and explanations are pleasingly accessible.
Featuring an emphasis on future consumers of psychological research and examples drawn from popular media, Research Methods in Psychology: Evaluating a World of Information develops students' critical-thinking skills as they evaluate information in their everyday lives. The Fourth Edition of this best-selling text takes learning to a new level for both consumers and producers by offering new content, interactive learning, and online assessment to help them master the concepts.
Another research design textbook, this one more specifically about neuroscience. My co-author, neuroscientist Robert Calin-Jageman, highly recommends it.
This third edition has clear and up-to-date discussions of issues such as phacking and publication bias that emphasise the need for Open Science. There’s a focus on effect sizes and confidence intervals, as in the new statistics. The book also describes strategies needed to enhance the rigor and reproducibility of neuroscience research.
Using engaging prose, Mary E. Harrington introduces neuroscience students to the principles of scientific research including selecting a topic, designing an experiment, analyzing data, and presenting research. This new third edition updates and clarifies the book's wealth of examples while maintaining the clear and effective practical advice of the previous editions. New and expanded topics in this edition include techniques such as optogenetics and conditional transgenes as well as a discussion of rigor and reproducibility in neuroscience research. Extended coverage of descriptive and inferential statistics arms readers with the analytical tools needed to interpret data. Throughout, practical guidelines are provided…
It’s the first introductory statistics text to use an estimation approach and explain Open Science from the very start. Estimation helps readers understand effect sizes, confidence intervals, and meta-analysis (‘the new statistics’). Open Science practices are new and exciting: They encourage replication and enhance the trustworthiness of research.
The book also explains traditional significance testing so students can understand old published research. There are numerous real research examples. The approach is highly visual, to make ideas accessible and memorable. The free open-source R-based esci (estimation statistics with confidence intervals) software makes concepts visually vivid and provides calculation and graphing facilities. It’s ideal for students at all levels and for researchers.
