The concept of statistical significance is central to planning, executing and evaluating A/B (and multivariate) tests, but at the same time it is the most misunderstood and misused statistical tool in internet marketing, conversion optimization, landing page optimization, and user testing.| Blog for Web Analytics, Statistics and Data-Driven Internet Marketing | Analy...
Short, understandable, yet accurate explanation of p-values and confidence intervals. Starting from the problem of random variability and building up with minimal jargon, this is the most accessible introduction to these basic statistical concepts. Understand the meaning and utility of confidence intervals and p-values in statistical hypothesis testing and estimation.| Blog for Web Analytics, Statistics and Data-Driven Internet Marketing | Analy...
Navigating the maze of A/B testing statistics can be challenging. This is especially true for those new to statistics and probability. One reason is the obscure terminology popping up in every other sentence. Another is that the writings can be vague, conflicting, incomplete, or simply wrong, depending on the source. Articles sprinkled with advanced math, calculus equations, and poorly-labeled graphs represent a major hurdle for newcomers.| Blog for Web Analytics, Statistics and Data-Driven Internet Marketing | Analy...
In math class, we spend a lot of time learning fractions because they are so important in everyday life (e.g., budgeting, purchasing at the grocery store). Fractions are also used extensively in UX research (e.g., the fundamental completion rate is a fraction), typically expressed as percentages or proportions. Unfortunately, fractions are also hard to learn, and as it turns out, they are not the easiest to statistically analyze.| MeasuringU