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xi | |
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xiv | |
Acknowledgements |
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xv | |
Getting Started |
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1 | (8) |
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PART I WHY WE USE STATISTICS |
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9 | (60) |
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1 How Statistics Support Science |
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11 | (14) |
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1.1 The Problem of Induction |
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12 | (3) |
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15 | (2) |
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17 | (1) |
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1.4 New Experimentalism in HCI |
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18 | (3) |
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21 | (1) |
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22 | (3) |
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25 | (13) |
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26 | (4) |
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2.2 Going beyond p-Values |
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30 | (3) |
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2.3 NHST and Severe Testing |
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33 | (2) |
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2.4 Honesty in Statistics |
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35 | (3) |
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38 | (15) |
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40 | (2) |
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42 | (2) |
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44 | (3) |
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3.4 The Bayesian Critique of Frequentism |
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47 | (1) |
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3.5 Being Careful: A Response to the Critique |
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48 | (2) |
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3.6 So, Frequentist or Bayesian? |
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50 | (3) |
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4 Effects: What Tests Test |
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53 | (16) |
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55 | (3) |
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58 | (1) |
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59 | (1) |
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4.4 Estimation and Significance |
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60 | (2) |
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4.5 Big, Small and Zero Effects |
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62 | (2) |
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4.6 Choosing Tests to See Effects |
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64 | (5) |
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PART II HOW TO USE STATISTICS |
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69 | (162) |
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5 Planning Your Statistical Analysis |
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71 | (9) |
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5.1 Principle 1: Articulation |
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73 | (2) |
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5.2 Principle 2: Simplicity |
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75 | (2) |
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77 | (2) |
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79 | (1) |
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6 A Cautionary Tail: Why You Should Not Do a One-Tailed Test |
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80 | (6) |
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81 | (1) |
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6.2 One-Tail Bad, Two-Tails Better |
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82 | (4) |
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86 | (9) |
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7.1 What Makes Data Normal? |
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86 | (4) |
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7.2 The Problems of Non-normal Data |
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90 | (1) |
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7.3 Testing for Normality |
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91 | (2) |
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93 | (2) |
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95 | (9) |
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96 | (2) |
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8.2 Sources and Remedies for Outliers |
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98 | (4) |
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99 | (1) |
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8.2.2 Mischievous Participants |
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99 | (1) |
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8.2.3 Faulty Study Design |
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100 | (1) |
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101 | (1) |
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102 | (2) |
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9 Power and Two Types of Error |
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104 | (10) |
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9.1 Type I and Type II Errors |
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105 | (1) |
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106 | (2) |
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9.3 Power and Sample Sizes |
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108 | (2) |
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9.4 Power and the Quality of Tests |
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110 | (2) |
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112 | (2) |
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10 Using Non-Parametric Tests |
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114 | (11) |
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10.1 The Mechanics of Ranks |
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115 | (2) |
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117 | (3) |
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117 | (2) |
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119 | (1) |
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120 | (2) |
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10.4 Reporting Non-Parametric Tests |
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122 | (1) |
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123 | (2) |
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125 | (14) |
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11.1 A Traditional t-Test |
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126 | (4) |
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130 | (1) |
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11.3 Location, Location, Location |
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131 | (1) |
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11.4 Trimmed and Winsorized Means |
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132 | (2) |
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134 | (1) |
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135 | (1) |
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136 | (3) |
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12 The ANOVA Family and Friends |
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139 | (16) |
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140 | (5) |
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145 | (2) |
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12.3 Robust Alternatives to ANOVA |
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147 | (4) |
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12.3.1 Non-Parametric Alternatives |
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147 | (2) |
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12.3.2 Changes of Location |
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149 | (1) |
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150 | (1) |
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151 | (4) |
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13 Exploring, Over-Testing and Fishing |
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155 | (12) |
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13.1 Exploring After a Severe Test |
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156 | (1) |
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157 | (2) |
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159 | (3) |
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13.3.1 ANOVA Can (Sometimes) Help |
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160 | (1) |
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13.3.2 Planned Comparisons |
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161 | (1) |
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13.3.3 The Bonferroni Correction |
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161 | (1) |
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13.3.4 Bayesian Methods Can Over-Test Too |
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162 | (1) |
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162 | (2) |
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13.5 Some Rules of Exploration |
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164 | (3) |
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14 When Is a Correlation Not a Correlation? |
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167 | (8) |
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14.1 Defining Correlation |
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168 | (2) |
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170 | (1) |
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171 | (2) |
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173 | (1) |
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174 | (1) |
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15 What Makes a Good Likert Item? |
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175 | (11) |
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15.1 Some Important Context |
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176 | (2) |
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15.2 Should Items Have a Midpoint? |
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178 | (2) |
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180 | (1) |
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15.4 Label All Options or Just End-Points? |
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181 | (1) |
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182 | (4) |
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16 The Meaning of Factors |
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186 | (18) |
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16.1 From Concepts to Items |
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188 | (2) |
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16.2 From Items to Factors |
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190 | (8) |
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16.2.1 The Methods of Factor Analysis |
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192 | (2) |
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194 | (4) |
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16.3 From Factors to Concepts? |
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198 | (2) |
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200 | (4) |
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17 Unreliable Reliability: The Problem of Cronbach's Alpha |
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204 | (1) |
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17.1 Reliability and Validity |
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205 | (1) |
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206 | (2) |
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208 | (1) |
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209 | (2) |
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211 | (3) |
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18 Tests for Questionnaires |
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214 | (1) |
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18.1 Testing Likert Items |
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215 | (1) |
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216 | (2) |
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218 | (4) |
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18.1.3 Which Test for Liken Items? |
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222 | (1) |
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222 | (2) |
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224 | (1) |
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225 | (4) |
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18.2.3 Which Test for Questionnaires? |
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229 | (1) |
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18.3 One Final Observation |
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229 | (2) |
Index |
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231 | |