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.

HardSystem Design

76 practiced

Create a detailed reproducibility checklist for ML + UX research artifacts required for an academic conference submission and for internal reproducibility. Include what to release (code, data or synthetic data, preprocessing scripts), environment specifications (Docker/conda), random seeds, hardware details, pre-registration docs, experiment logs, and evaluation scripts so reviewers can reproduce results end-to-end.

MediumTechnical

80 practiced

A model trained on users from Region A performs poorly after deployment to Region B. Design a set of offline and online tests to diagnose whether the drop is caused by distribution shift, label noise, or cultural differences. Describe dataset partitioning, stress tests (e.g., counterfactuals), feature-level analysis, and criteria for rollback vs retraining.

EasyTechnical

65 practiced

As a Research Scientist on a UX-focused team, clearly define and contrast 'hypothesis-driven' research and 'exploratory' research. For each: (1) state the primary goal, (2) name typical methods and deliverables, (3) give a concrete product-UX example, and (4) explain when you would choose one approach over the other in a research roadmap.

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.

HardTechnical

68 practiced

Describe a rigorous protocol to quantify the sensitivity of a new ML architecture to hyperparameters and training choices. Include experiment design (grid/random/Bayesian search), number of repetitions, variance estimation, global sensitivity analysis (e.g., Sobol), visualization techniques (response surfaces, violin plots), and how to report stability and uncertainty in a paper.

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