Statistics for UX

Collecting and reporting numbers is the backbone of quantitative UX. Learn the statistics needed to understand and analyze the numbers you get from UX research, and the types of predictions you can make from such numbers.

Syllabus

What you will learn

Understand essential statistics concepts in a UX context

Center and variability of a data distribution
Continuous, discrete, and binary metrics
True-score theory
Measurement error
Confidence intervals
Statistical significance

Determine how much you can trust your data

Why a design with better metrics might not actually be better
How to verify whether the numbers from a study are trustworthy
How to protect your quantitative study from measurement errors
Which questions to ask when presented with numbers

Learn statistics methods and how to apply them to UX data from a variety of sources

Master statistical tools for analyzing UX performance metrics (success rates, task-completion times) and self-reported metrics (single-ease questions, SUS, NPS)
Compare differences between designs
Understand if your design matches a desired benchmark
Decide how many participants to recruit for your study to get reliable results
Structure research studies to maximize statistical power and relevance