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      Copyright © 2020 by Morgan & Claypool

      All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted in any form or by any means—electronic, mechanical, photocopy, recording, or any other except for brief quotations in printed reviews, without the prior permission of the publisher.

      Statistics for HCI: Making Sense of Quantitative Data

      Alan Dix

       www.morganclaypool.com

      ISBN: 9781681737430 paperback

      ISBN: 9781681737447 ebook

      ISBN: 9781681737454 hardcover

      DOI 10.2200/S00974ED1V01Y201912HCI044

      A Publication in the Morgan & Claypool Publishers series

       SYNTHESIS LECTURES ON HUMAN-CENTERED INFORMATICS

      Lecture #44

      Series Editor: John M. Carroll, Penn State University

      Series ISSN

      Print 1946-7680 Electronic 1946-7699

       Statistics for HCI

       Making Sense of Quantitative Data

      Alan Dix

      Computational Foundry, Swansea University, Wales

       SYNTHESIS LECTURES ON HUMAN-CENTERED INFORMATICS #44

       ABSTRACT

      Many people find statistics confusing, and perhaps even more confusing given recent publicity about problems with traditional p-values and alternative statistical techniques including confidence intervals and Bayesian statistics. This book aims to help readers navigate this morass: to understand the debates, to be able to read and assess other people’s statistical reports, and make appropriate choices when designing and analysing their own experiments, empirical studies, and other forms of quantitative data gathering.

       KEYWORDS

      statistics, human–computer interaction, quantitative data, evaluation, hypothesis testing, Bayesian statistics significance testing, p-hacking

       Contents

       Preface

       Acknowledgments

       1 Introduction

       1.1 Why are probability and statistics so hard?

       1.1.1 In two minds

       1.1.2 Maths and more

       1.2 Do you need stats at all?

       1.3 The job of statistics –from the real world to measurement and back again

       1.3.1 The ‘real’ world

       1.3.2 There and back again

       1.3.3 Noise and randomness

       1.4 Why are you doing it?8

       1.4.1 Empirical research

       1.4.2 Software development

       1.4.3 Parallels

       1.5 What’s next

       PART I Wild and Wide –Concerning Randomness and Distributions

       2 The unexpected wildness of random

       2.1 Experiments in randomness

       2.1.1 Rainfall in Gheisra

       2.1.2 Two-horse races

       2.1.3 Lessons

       2.2 Quick (and dirty!) tip

       2.2.1 Case 1 –small proportions

       2.2.2 Case 2 –large majority

       2.2.3 Case 3 –middling

       2.2.4 Why does this work?

       2.2.5 More important than the math

       2.3

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