Research Hypothesis Development and Testing Questions
Learn to develop clear research hypotheses and design studies to test them. Practice distinguishing between open-ended exploratory research and hypothesis-driven research. Discuss how you develop hypotheses from prior knowledge, design documentation, or preliminary research. Explain how you structure research to test hypotheses rigorously.
HardTechnical
77 practiced
Design an experimental pipeline to measure and test differences in cognitive load between two interfaces. Include task design, physiological measures you might collect (EEG, pupillometry, heart rate variability), data synchronization with task events, preprocessing and feature extraction steps, statistical tests, and controls for confounds like stress and fatigue.
MediumTechnical
79 practiced
Define novelty effect, primacy bias, and recency bias in user research. Propose experimental design and analysis techniques to detect and mitigate these biases in both short-term lab studies and longer field experiments. Give at least three concrete design choices or analysis corrections.
MediumSystem Design
71 practiced
Design an online experiment to evaluate a new recommender algorithm intended to increase time-on-site for a platform with 10 million monthly active users. Provide: (1) explicit null and alternative hypotheses, (2) a primary metric and two guardrail metrics, (3) randomization/bucketing strategy, (4) sample-size/power estimation approach, (5) rollout and monitoring plan, and (6) stopping rules.
EasyTechnical
85 practiced
Define internal validity and external validity in the context of UX experiments. Provide a UX example where an experiment has high internal validity but low external validity, explain why that occurs, and suggest at least three concrete methods to increase external validity without sacrificing too much internal control.
MediumTechnical
74 practiced
Explain Bayesian A/B testing versus frequentist hypothesis testing in the context of online UX experiments. Compare interpretation of results, stopping rules, types of intervals reported, required assumptions, computational complexity, and scenarios where a Bayesian approach may be preferred for product decisions.
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