{"id":112,"date":"2012-04-17T23:15:00","date_gmt":"2012-04-17T23:15:00","guid":{"rendered":"http:\/\/measuringu.com\/two-proportions\/"},"modified":"2022-03-21T18:27:00","modified_gmt":"2022-03-22T00:27:00","slug":"two-proportions","status":"publish","type":"post","link":"https:\/\/measuringu.com\/two-proportions\/","title":{"rendered":"6 Proportions to Compare when Improving the User Experience"},"content":{"rendered":"
<\/a>One of the simplest ways to measure any event is a binary metric coded as a 1 or 0.<\/p>\n Such a metric represents the presence or absence of just about anything of interest: Yes\/ No,\u00a0 Pass\/ Fail, Purchase\/No Purchase, On\/Off.<\/p>\n Fundamentally the binary system is at the heart of computing as we know it.<\/a> It also plays a critical role in user research.<\/p>\n Binary measures can even apply to traditionally qualitative research such as noting whether a user mentions distrust of sales people or marketing materials<\/a> in an interview.<\/p>\n By adding up all the 1’s and dividing by the total number observed you get a proportion. So if 5 out of 10 users mention distrusting marketing materials in an interview you get a proportion of 5\/10 = .5 which is often expressed as a percentage 50%.<\/p>\n You can use proportions to help make data driven decisions just about anywhere: Which design converts more? Which product is preferred? Does the new interface have a higher completion rate? What proportion of users had a problem registering?<\/p>\n When it comes to comparing independent proportions you’ll need to conduct a statistical test called the 2 proportion test (which is equivalent to the Chi-Square test).<\/p>\n We talk a lot about comparing two proportions in our book Quantifying the User Experience (Chapter 5)<\/a> and recommend a slight adjustment to the typical formulas you might be familiar with so it works for small and large sample sizes.<\/p>\n To make it easier to compare proportions, I’ve created a simple online web-calculator<\/a> which will do the statistical calculations for you. Just enter two proportions to see if the difference between them is more likely due to chance or more likely a legitimate difference.<\/p>\n Here are 6 examples of proportions and the statistical results to get you thinking:<\/p>\n One of the simplest ways to measure any event is a binary metric coded as a 1 or 0. Such a metric represents the presence or absence of just about anything of interest: Yes\/ No,\u00a0 Pass\/ Fail, Purchase\/No Purchase, On\/Off. Fundamentally the binary system is at the heart of computing as we know it. 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