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Statistics half-day course at CHI2017- Making Sense of Statistics in HCI: From P to Bayes and Beyond

Key information

Event date: 10 May 2017
Venue: Denver, USA
Source: Instructor
Submitted by: Alan Dix
Website: http://alandix.com/statistics/course/

Event description

Do you find statistics confusing? This course fills the gap between the 'how to' knowledge in basic statistics courses and deep understanding| from traditional hypothesis testing to Bayesian stats.

Many find statistics confusing, and perhaps more so given recent publicity of problems with traditional p-values and alternative statistical techniques including confidence intervals and Bayesian statistics.

This course aims to help attendees navigate this morass: to understand the debates and more importantly make appropriate choices when designing and analysing experiments, empirical studies and other forms of quantitative data. It is intended to fill the gap between the 'how to' knowledge in basic statistics courses and deep understanding.

You won't learn a hundred new statistical techniques, but you will find out what those you have heard of and use actually mean.

Where and When

During CHI2107, Denver - Wednesday 10th May 2017 at 2:30pm see CHI2017 courses: https://chi2017.acm.org/courses.html

Also before Tiree Tech Wave, Scotland - Wednesday, 22nd March 2017

Who

The course is intended for both experienced researchers and students who have already, 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. The course will assume some familiarity with statistical concepts theoretical or practical, for example, the use of t-tests or similar techniques. There will be occasional formulae, but the focus of the course is on conceptual understanding not mathematical skills.

The instructor is Alan Dix, HCI educator, researcher and mathematician.

What

The course is divided into four main parts: 

  • Wild and wide - Exploring the nature of randomness, uncertainty and 'distributions' 
  • Doing it - Deciding between alternative statistical analyses from the ubiquitous 'p' to Bayesian methods. 
  • Gaining power - Learning how to design studies to avoid the dreaded 'too few participants' problem 
  • So what? - Making sense of the data you get from your studies and avoiding the pitfalls.