Pass or fail? Binomial-related hypothesis testing and confidence intervals using independent samples
Abstract
In this chapter we introduce what to do when we are looking at pass/fail data and simple preference data. Pass/fail is a common scenario for UX researchers, where they are asked to make conclusions from binomial data (i.e., two outcomes) rather than normally distributed data. For example, using the logic of hypothesis testing to determine whether the (true) proportion who pass two or more tasks are the same or different, or to determine whether the pass/fail rate differs for the same task using two or more Web sites. In addition, a UX researcher may need to find a confidence interval for the true proportion of people who will pass ...
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