{"id":102,"date":"2017-05-15T11:38:36","date_gmt":"2017-05-15T11:38:36","guid":{"rendered":"https:\/\/alandix.com\/statistics\/?page_id=102"},"modified":"2017-08-18T17:41:02","modified_gmt":"2017-08-18T17:41:02","slug":"so-what","status":"publish","type":"page","link":"https:\/\/alandix.com\/statistics\/course\/so-what\/","title":{"rendered":"So what? \u2013 making sense of results"},"content":{"rendered":"

You have done your experiment or study and have your data \u2013 what next, how do you make sense of the results? In fact one of the best ways to design a study is to imagine this situation before<\/em> you start!<\/p>\n

This part will address a number of questions to think about during analysis (or design) including: Whether your work is to test an existing hypothesis (validation) or to find out what you should be looking for (exploration)? Whether it is a one-off study, or part of a process (e.g. ‘5 users’ for iterative development)? How to make sure your results and data can be used by others (e.g. repeatability, meta analysis)? Looking at the data, and asking if it makes sense given your assumptions (e.g. Fitts’ Law experiments that assume index of difficulty is all that matters). Thinking about the conditions \u2013 what have you really<\/em> shown \u2013 some general result or simply that one system or group of users is better than another?<\/p>\n

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  1. why are you doing it?<\/a><\/li>\n
  2. look at the data<\/a><\/li>\n
  3. visualise carefully<\/a><\/li>\n
  4. what have you really shown?<\/a><\/li>\n
  5. diversity: individual and task<\/a><\/li>\n
  6. mechanism<\/a><\/li>\n
  7. building for the future<\/a><\/li>\n<\/ol>\n