ds-analysis-plan

To create a reproducible analysis plan, convert your research question into an explicit execution plan that defines cohorts, time windows, metrics, and validation checkpoints before touching queries or notebook cells. This allows another analyst to rebuild and rerun the workflow from scratch.

Experiment design validation requires diagnostics like balance checks, SRM, and invariants to ensure cohort consistency. You need it before major transformations to prevent late discovery failures in research workflows. It sets a validation level and verification mode to control output strictness.

Specify metric definitions and statistical methods by building a concrete metric hierarchy tied to your randomization units. Include explicit exclusions and defined task granularity with observable artifacts for downstream consistency. This ensures outputs remain decision-ready.

Notebook execution planning supports runtime observability by requiring compact stage, timing, and row-count checkpoints. It sets explicit progress expectations to avoid silent long-running work during analysis. This provides runtime visibility across all SQL or pandas steps.

Analysis planning for experiment workflows requires clear escalation triggers tied to pass/fail interpretation. Without predefined escalation triggers, you cannot properly control validation levels or verification modes for decision-ready outputs. Escalation triggers ensure proper pass/fail interpretation.

Without a structured plan header, transformations lack defined task granularity and expected output shapes, risking silent failures. You lose the ability to produce a final memo that another analyst can rebuild from scratch. This leads to late discovery failures in experiment workflows.