Statistics for HCI

Making Sense of Quantitative Data
2. Auflage 2026
ISBN: 978-3-032-35279-8
Verlag: Springer

Buch, Englisch, 288 Seiten, Format (B × H): 168 mm x 240 mm

Reihe: Synthesis Lectures on Human-Centered Informatics

This revised second edition enables readers to make sense of the statistics necessary to interpret quantitative data and to understand the limitations of numbers and how quantitative and qualitative methods can work together. It fills the gap between ‘how-to’ knowledge in basic statistics texts and a practical understanding of what those statistics mean to researchers and practitioners in human-computer interaction (HCI). The book covers the occasional formulae but primarily focuses on developing a conceptual understanding rather than mathematical skills. In doing so, it equips readers to better understand reports, data, and academic papers that use statistical techniques and to critically assess the validity of their results and how they may apply to their own practice or research. Most importantly, readers will be better placed to design studies that efficiently use available resources and appropriately, effectively, and reliably analyze the results. Later chapters present aspects of statistical ‘craft’ skills that are rarely considered in standard textbooks and explore the interaction between statistical methods and the use of Artificial Intelligence (AI) and Machine Learning (ML). By the end of the book, readers should have a richer understanding of: (1) the nature of random phenomena and different kinds of uncertainty; (2) the different options for analyzing data and their strengths and weaknesses; (3) ways to design studies and experiments to increase ‘power’—the likelihood of successfully uncovering real effects; and (4) the pitfalls to avoid and issues to consider when dealing with empirical data. This book is intended for both experienced researchers and students who have already engaged, or intend to engage, in quantitative analysis of empirical data or other forms of statistical analysis. It will also be of value to practitioners using quantitative evaluation. 

Zielgruppe

Professional/practitioner

Autoren/Hrsg.

Dix, Alan

Fachgebiete

• Mathematik | Informatik EDV | Informatik Informatik Mensch-Maschine-Interaktion
• Mathematik | Informatik Mathematik Stochastik
• Technische Wissenschaften Elektronik | Nachrichtentechnik Elektronik
• Mathematik | Informatik EDV | Informatik Business Application Mathematische & Statistische Software
• Mathematik | Informatik EDV | Informatik Informatik Mathematik für Informatiker

Inhaltsverzeichnis

Preface.- Introduction.- PART I: Wild and Wide—Concerning Randomness and Distributions.- The unexpected wildness of random.- Properties of randomness.- Characterizing randomness through probability distributions.- PART II: Doing It—If Not p Then What.- Probing the unknown.- Traditional statistics.- Bayesian methods.- Statistical Methods Interacting with AI and ML.- Common issues.- Differences and distinctions.- PART III: Going Large—Computation, Big Data, and AI.-Empirical and Simulation Methods: Beyond Fixed Tests.- Big Data: Seeing Through the Forest.- AI: Intelligence But Not as We Know It.- PART IV—Design and Interpretation.- Gaining power: The dreaded `too few participants'.- So what?: Making sense of results.- Moving forward: The future of statistics in HCI.

Über Autor(innen)

Alan Dix is an Emeritus Professor at Cardiff Metropolitan University and at Swansea University, Wales, where he was previously Director of the Computational Foundry. He is well known for an HCI textbook and research in HCI including CSCW, mobile interfaces, technical creativity and some of the earliest work on privacy and the ethical implications of intelligent algorithms. More recent work includes community engagement, especially in rural areas, renewed work on the boundaries between HCI and AI, and his thousand-mile research walk around Wales, which generated substantial quantitative and qualitative open research data, from blogs to biodata. Before he was in HCI, Alan was a mathematician and represented the UK in the International Mathematical Olympiad. He has practiced as a professional statistician and applied mathematician including work on modelling agricultural crop sprays, medical statistics and undersea cable detection. Within HCI these skills have been applied in his foundational work on formal methods for interactive systems, the use of Bayesian techniques in education, random sampling for visualization of big data and uncertainty, and analysis of potential bias against human/applied areas in REF, the UK research assessment exercise.
This unusual combination of skills and experience gives Alan a unique insight into the challenges and problems of applying statistics to HCI data.