Statistics for HCI

Beschreibung

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. 

In addition, this book: 

• Enables readers to make sense of the statistics necessary to interpret quantitative data
• Fills the gap between theoretical and practical statistics for HCI researchers and practitioners
• Equips readers to design efficient studies as well as effectively and reliably analyze the results

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. 

Autor*in

Alan Dix

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Quantitative Data Evaluation Hypothesis Testing Bayesian Statistics Significance Testing p-hacking Statistics for HCI Statistics for AI Statistics for ML Empirical Methods Big Data

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