Experiment design for future bets

Problem

Future investments in the graph library's interaction and accessibility capabilities — such as advanced keyboard-driven chart exploration, sonification for visually impaired users, or touch-optimized mobile interactions — carry high uncertainty about user value. Without designed validation experiments (prototypes, A/B tests, user research protocols), the team would have to either commit significant engineering resources to features that may not resonate or avoid ambitious interaction work entirely. Experiment designs created now establish a playbook for testing future bets cheaply before committing to full implementation.

Context

• The larger future bets for visual language, chart defaults, interaction behavior, and differentiated styling should be validated with scoped experiments before they absorb major implementation effort or become roadmap commitments.
• This task should design the experiments, not run them: define hypotheses, success signals, cheap prototypes or evaluation methods, and the decision rule for what happens next.
• Expected touchpoints include dataface/core/render/chart/, chart design docs, examples, and visualization test coverage, opportunity/prerequisite notes, eval or QA harnesses where relevant, and any external dependencies required to run the experiments.

Possible Solutions

• A - Rely on team intuition to pick which future bet to pursue: fast, but weak when the bets are expensive or high-risk.
• B - Recommended: design lightweight validation experiments for the strongest bets: specify hypothesis, method, scope, evidence, and the threshold for continuing or dropping the idea.
• C - Build full prototypes for every future direction immediately: rich signal, but far too expensive for early-stage uncertainty.

Plan

1. Choose the future bets for visual language, chart defaults, interaction behavior, and differentiated styling that are both strategically important and uncertain enough to justify explicit experiments.
2. Define the hypothesis, cheapest credible validation method, required inputs, and success/failure signals for each experiment.
3. Document the operational constraints, owners, and follow-up decisions so the experiment outputs can actually change roadmap choices.
4. Rank the experiments by cost versus decision value and sequence the first one or two instead of trying to validate everything at once.

Implementation Progress

Review Feedback

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