One of the more challenging things about learning math in general (and statistics in particular) is how the formulas, often with Greek symbols, translate to things we can see and experience. The abstractness of these formulas often means we just have to take them at face value, believing that someone smarter than us made sure
What’s the value of a degree? While most discussions on the cost and value of a university education focus on the undergraduate degree, getting a bachelor’s degree isn’t really a question that’s asked in the UX world. Almost all UX professionals (93%–97%) surveyed over the last decade have at least a bachelor’s degree, suggesting that
What sample size should you use for a problem discovery (formative) usability study? In practice, the answer is based on both statistics AND logistics. A statistical formula will tell you an optimal number to select. But the real-world logistical constraints of budgets, recruiting challenges, and time will often dictate the maximum number of participants you
“How many participants should you run in a usability study?” How many times have you heard that question? How many different answers have you heard? After you sift through the non-helpful ones, probably the most common answer you’ve heard is five. You might have also heard that these “magic 5” users can uncover 85% of
The standard deviation is the most common way of measuring variability or “dispersion” in data. The more the data is dispersed, the more measures such as the mean will fluctuate from sample to sample. That means higher variability (higher standard deviations) requires larger sample sizes. But exactly how much do standard deviations—whether large or small—impact
Are customers more satisfied this quarter than last quarter? Do users trust the brand less this year than last year? Did the product changes result in more customers renewing their subscriptions? When UX researchers want to measure attitudes and intentions, they often ask respondents to complete multipoint rating scale items, which are then compared with
What is the impact if you sample a lot of your population in a survey? Many statistical calculations—for example, confidence intervals, statistical comparisons (e.g., the two-sample t-test), and their sample size estimates—assume that your sample is a tiny fraction of your population. But what if you have a relatively modest population size (e.g., IT decision-makers
Near the top of the list of concerns people have when using statistics with UX data is what to do with non-normal data. If you remember only a few things from statistics class, you might recall something about data needing to look like the infamous bell curve; more specifically, it needs to be normally distributed.
If you took an intro to stats class (or if you know just enough to be dangerous), you probably recall two things: something about Mark Twain’s “lies, damned lies …,” and that your data needs to be normally distributed. Turns out both are only partly true. Mark Twain did write the famous quote, but he
Statistics doesn’t have a Magna Carta, constitution, or bill of rights to enumerate laws, guiding principles, or limits of power. There have been attempts to articulate credos for statistical practice. Two of the most enduring ones are based on the work by Robert P. Abelson, a former statistical professor at Yale. If Abelson wasn’t the
