All bites
The whole library, newest first. Filter by what you are here for, or pick a topic if you already know.
4247 bites
Page 54
Discourse Analysis in UX Research
Discourse analysis treats language as a design artifact, revealing how users construct meaning. Apply it to interviews and support logs to find unstated needs. Footgun: mining for confirming quotes rather than analyzing how the narrative is built.
Service Blueprint: The Wiring Diagram Behind User Journeys
A service blueprint X-rays the hidden machinery behind a customer journey. Use it for omnichannel services crossing departments. The footgun is producing generic diagrams untethered from a specific business goal like reducing redundancy.
Stevens' Four Levels of Measurement
Data is not just numbers; Stevens' levels classify the nature of information each variable holds. In UX research, this determines whether you can average feedback or only count it. Treating every rating scale as a ratio number wrecks your analysis.
Normal Distribution: Two Parameters, One Bell Curve
A normal distribution is a bell curve for real-valued variables, defined by mean and standard deviation. Use it when values cluster around a center. The footgun is forcing normality on skewed or bounded data, which corrupts the probability density.
Correlation Does Not Mean Causation
Two metrics moving together does not mean one drives the other. In UX research, a spike in clicks after a redesign does not prove the redesign caused it. The footgun is treating every coincidence as proof your change worked.
Inferential Statistics: From Sample to Population
Inferential statistics turns a small user sample into a claim about millions: you test hypotheses and estimate population properties without measuring everyone. The footgun is forgetting your data is just a sample and declaring truth about all users.
T-test: Are Two Groups Actually Different?
A t-test asks whether the gap between two groups is a real signal or just sampling noise. UX researchers use it to compare task times or conversion rates between designs. The footgun is trusting results from tiny samples where the math gives false confidence.
Effect Size: How Large Is the Difference?
Effect size measures how much a phenomenon actually matters, not just whether it exists. In UX research, it quantifies the real-world impact of a design change beyond hypothesis testing. Ignoring it leads to chasing tiny, meaningless wins.
Topline Report: The Research Snapshot
A topline report is the snapshot of a study: goals, key learnings, and context in one doc. Teams use it to align stakeholders days after research, sometimes before full analysis. Never skip the disclaimer or stakeholders will treat impressions as final.
Insight Wall: Synthesize Mixed Research Data
An insight wall clusters mixed research data so teams spot patterns interviews alone hide. Use it when notes, analytics, and stakeholder input must become design decisions. Teams often collect data but skip synthesis, so insights never form.
HMW Questions: From Research Insights to Design Ideas
HMW questions turn research insights into open design challenges that keep ideation focused on real user problems. Teams use them after discovery to prevent pet solutions.
UX Highlight Reels: Show, Don't Tell
A highlight reel is a shortcut to empathy: short clips that make stakeholders feel the problem. Use them to win executive buy-in when charts fail to stick. The footgun is curating only positive clips; without struggle it reads as marketing, not research.
Exposure Hours
Exposure hours measure the time each team member spends directly observing real users. Research by UIE found teams that watch users at least two hours every six weeks make markedly better design decisions, because firsthand empathy beats secondhand reports.
Data Visualization for Qualitative Data
Visualizing qualitative data turns coded themes, quotes, and patterns from interviews into affinity maps, theme matrices, and journey artifacts. It makes non-numeric findings scannable and persuasive without distorting nuance into false precision through…
ResearchOps: The Pit Crew for User Research
ResearchOps is the backstage crew that makes user research scale. It provides roles, tools, and processes so researchers focus on insights, not logistics. Teams often assume researchers self-manage operations, which bottlenecks research as companies grow.
Research Democratization: Scale Without Diluting Quality
Research democratization is controlled expansion, not chaos. Non-researchers run simple interviews while pros own complex design. It helps teams move fast when researchers are scarce, but the footgun is untrained staff picking methods or running quant studies.
ResearchOps Maturity Matrix: Built for Operations
UX maturity scores insights; ResearchOps maturity scores the factory producing them. Use it to audit governance, participant pipelines, and tooling strategy. The mistake is judging research quality instead of operational infrastructure.
Build-Measure-Learn Loop: Experiment Over Intuition
Ship a small test, watch real reactions, and steer the next version by data instead of gut instinct. Use it when you must know fast if a business model is viable. The footgun is calling the first release a product instead of an experiment.
Proto-Persona: Sketch Users from Team Assumptions
A proto-persona sketches your team's user assumptions in a quick workshop, creating shared targets without new research. It aligns Lean teams, but the footgun is treating these guessed profiles as facts rather than hypotheses.
Assumption Mapping: De-Risk Before You Build
Assumption mapping treats product ideas as bundles of unproven bets. Teams sort beliefs into desirability, feasibility, and viability to find the riskiest ones. It prevents shipping features nobody wants. The footgun is treating the map as the finish line.