A New Formula for Quantitative UX Decision Making | UX Magazine - 0 views
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A New Formula for Quantitative UX Decision Making by Jeff Sauro, Jim Lewis Comments (0) Share on deliciousShare on diggShare on emailShare on hackernewsShare on redditShare on facebook_like32 Imagine a formula that would allow you to take data from a very small pool of users (often as few as 8; possibly as few as 3) and figure out why, for instance, Autodesk customers are calling support, whether Budget.com visitors can rent a car in under a minute, or why cardholders were reluctant to use a mobile payment site. Such a formula exists, and it’s not some abstract “formula for success” in management strategy or a design technique. We’re talking about a mathematical formula that’s easy to use but can transform the way you measure and manage the user experience. The formula is called the Adjusted-Wald Binomial Confidence Interval (“Adjusted-Wald Interval” for short), but its name isn’t as important as what it can do. Its power is in helping estimate the behavior of an entire user population, even when the sample size is small. It does this by taking a simple proportion as input and producing a confidence interval. For example, suppose 10 users have attempted a task and 7 completed it successfully. The simple successful completion rate is 70%. But, given such a small sample size, how can you have any faith in the result? Would it be reasonable to expect to get exactly 7,000 succe
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A New Formula for Quantitative UX Decision Making by Jeff Sauro, Jim Lewis Comments (0) Share on deliciousShare on diggShare on emailShare on hackernewsShare on redditShare on facebook_like 32 Imagine a formula that would allow you to take data from a very small pool of users (often as few as 8; possibly as few as 3) and figure out why, for instance, Autodesk customers are calling support, whether Budget.com visitors can rent a car in under a minute, or why cardholders were reluctant to use a mobile payment site. Such a formula exists, and it's not some abstract "formula for success" in management strategy or a design technique. We're talking about a mathematical formula that's easy to use but can transform the way you measure and manage the user experience. The formula is called the Adjusted-Wald Binomial Confidence Interval ("Adjusted-Wald Interval" for short), but its name isn't as important as what it can do. Its power is in helping estimate the behavior of an entire user population, even when the sample size is small. It does this by taking a simple proportion as input and producing a confidence interval. For example, suppose 10 users have attempted a task and 7 completed it successfully. The simple successful completion rate is 70%. But, given such a small sample size, how can you have any faith in the result? Would it be reasonable to expect to get exactly 7,000 successes if the sample size was 10,000? Probably not, but how far off might it be?
