More in UX Research — page 11
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.
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: 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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Grounded Theory: Build Theory from Raw Data
Grounded theory builds hypotheses from data, not from prior assumptions. UX researchers reach for it when exploring messy human behavior without existing models. The footgun: forcing early categories onto data instead of letting patterns emerge inductively.
Narrative Analysis: How People Build Meaning
Narrative analysis treats life as an edited story; it reads journals and interviews for the meaning a person constructs, not raw facts. Use it when you need to understand why a belief formed. Never lift a quote from its story; context is the entire signal.
Insight Statements: Frame Problems, Not Solutions
Insight statements frame what users must achieve, not how. Teams use them in design thinking's define stage to align on the right problem before ideating. The footgun is writing solutions like 'needs a dashboard' instead of goals like 'needs to compare'.
Content Analysis: Study Texts Without Interference
Content analysis extracts patterns from existing artifacts without disturbing subjects. Social scientists apply it systematically to speeches, photographs, and essays. The common footgun is treating it as writing-only and ignoring visual or spoken media.
AEIOU: Five Buckets for Field Notes
AEIOU sorts notes into five buckets—Activities, Environments, Interactions, Objects, Users—so patterns emerge from chaos. Use it during field studies to categorize raw data. The footgun is treating its categories as rigid rules not editable heuristics.