Every usability test produces evidence, but not all evidence looks the same. Some of it is numeric — how many participants finished a task, how long they took, how many errors they made, how they rated the system at the end. Some of it is observational — what a participant said while hesitating, where they looked for a button that wasn't there, what assumption led them down the wrong path. This topic builds the shared vocabulary you need to talk about both kinds of data and to know which kind answers which question.
The distinction matters because the two types of data do different jobs. Quantitative measures such as task completion rate, error rate, time on task, and satisfaction scores let you compare designs, track progress across iterations, and describe the size of a problem. Qualitative findings explain why the numbers look the way they do and point toward a fix. In this module you will need both: numbers to prioritize and justify, observations to diagnose and redesign.
As you watch the videos below, pay attention to three things. First, notice when a speaker is talking about a small qualitative study versus a larger measurement study, since the sample size changes what a number is allowed to mean. Second, note the exact definition of each metric — what counts as "complete," what counts as an "error" — because sloppy definitions produce misleading results. Third, watch how satisfaction is treated as its own dimension, separate from whether a user actually succeeded.
This short video from Nielsen Norman Group discusses what happens when you collect numeric measures during a qualitative usability study, where the goal is insight rather than statistical precision. It addresses how metrics gathered from a handful of individual participants can be used to describe how each person performed and to support the narrative of the findings, rather than being reported as reliable averages. Expect guidance on the limits of small-sample numbers and on keeping metrics in service of the story.
It directly addresses the quantitative versus qualitative distinction that anchors this topic, and it comes from a widely cited usability research organization, so the framing matches standard professional practice.
This video introduces benchmark usability testing: studies designed to measure key performance indicators of an interface so that performance can be compared over time or against another design. It is likely to cover the kinds of KPIs typically tracked in such studies — including success or completion rates, time on task, errors, and satisfaction — and how a benchmark is structured so results remain comparable across rounds. The emphasis is on measurement with a purpose rather than numbers for their own sake.
Benchmarking is where completion rates, error rates, and time on task are used most rigorously, so this video ties several of the subtopics together and shows why consistent metric definitions matter for iterative redesign.
The longest of the three videos, this one explains the System Usability Scale, a standardized ten-question survey given at the end of a usability session to capture perceived usability. It covers what the instrument is, how it is administered, and how the resulting score is interpreted as a measure of a user's subjective experience. Expect discussion of why a standardized, well-established questionnaire is preferable to ad hoc satisfaction questions.
It gives concrete, widely adopted grounding for the satisfaction scores subtopic and demonstrates how a subjective impression can be turned into a comparable number for use across design iterations.
Together these videos establish the measurement vocabulary the rest of the module depends on. Understanding which data are qualitative and which are quantitative, and how completion, error, time, and satisfaction measures are defined, is what allows you to read a set of test results and spot recurring patterns and failure points (MO1). The benchmarking and metrics videos show how frequency and magnitude are captured numerically, which is the basis for ranking issues by severity and user impact when deciding what to fix first (MO2). The emphasis on consistent, repeatable measures — especially standardized satisfaction scoring — supports iterative redesign by giving you comparable evidence across rounds and a documented rationale for each change you make (MO3).
These videos were chosen from YouTube on October 3, 2026. Each has captions; the player turns them on. Videos on YouTube can be changed or removed — if one no longer plays, tell your instructor. If a player does not appear, use its Watch on YouTube link.