{"id":616,"date":"2020-09-30T18:21:22","date_gmt":"2020-09-30T18:21:22","guid":{"rendered":"https:\/\/alandix.com\/statistics\/?page_id=616"},"modified":"2020-10-05T10:33:43","modified_gmt":"2020-10-05T10:33:43","slug":"mobilehci2020","status":"publish","type":"page","link":"https:\/\/alandix.com\/statistics\/events\/mobilehci2020\/","title":{"rendered":"MobileHCI2020 Tutorial"},"content":{"rendered":"

Tutorial at MobileHCI2020 \u2013 Understanding Statistics<\/h1>\n
\n
\n
\n
\n
\n
\n
\n
\n

Monday 5th of October 2020, 1pm\u20133pm (CEST)\u00a0 \u2013\u00a0 12pm\u20132pm (UK time)<\/h4>\n

https:\/\/mobilehci.acm.org\/2020\/tutorials\/<\/a><\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/section>\n

\n
\n
\n

Handouts:<\/strong> the plains of Nali\u00a0<\/a> |\u00a0 two horse races<\/a>\u00a0 \u00a0|\u00a0 a little experiment<\/a><\/p>\n

Many researchers find statistics confusing.\u00a0 The aim of this tutorial is to help attendees understand what the statistics they see around them actually mean. It fills the gap between the \u2018how to\u2019 knowledge in basic statistics courses and deep understanding, from traditional hypothesis testing to Bayesian techniques.\u00a0 This tutorial builds on previous tutorials and master classes, and his recently published book \u201cStatistics for HCI: Making Sense of Quantitative Data<\/em>\u201d and also extensive freely available online materials including videos and glossary.<\/p>\n

Many of the issues discussed will not be found in a traditional textbook or statistics course, so there will be things to learn for those already using statistics in their work.\u00a0 However, the material will be expressed in ways that do not assume any existing statistical expertise.\u00a0 It includes aspects of statistical `craft\u2019 skill that you will not find in conventional material.<\/p>\n

Intended Audience<\/h1>\n

This tutorial is intended for both experienced researchers and those 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. There will be occasional formulae, but the focus is on conceptual understanding, not mathematical skills.<\/p>\n

Main Topics<\/h1>\n

As there are extensive online materials available and the book itself, the tutorial can follow the T-model of teaching, combining a broad roadmap of the area, with depth in a few topics.\u00a0 If they can feel mastery of the material and know what is available, they can fill in gaps in a \u2018just in time\u2019 manner when they wish.<\/p>\n

Roadmap topics (likely foci in bold)<\/h2>\n

Understanding randomness<\/em><\/p>\n