Statistical software is often stochastic, but your test suite should still produce deterministic pass or fail outcomes.

Seed Policy Is Necessary But Not Sufficient

Fixed random seeds help reproducibility, but they do not solve everything. You still need:

  1. Stable data generation procedures.
  2. Versioned dependencies.
  3. Explicit tolerances for numeric comparisons.

Without these, tests may flicker across machines or compiler settings.

Tolerance Design

Avoid exact equality for floating-point outputs. Use tolerances informed by algorithm scale:

  1. Absolute tolerance for near-zero quantities.
  2. Relative tolerance for scale-dependent outputs.
  3. Domain-aware thresholds for inference summaries.

Bad tolerances either hide regressions or create noisy failures.

Layered Test Stack

A practical stack has three layers:

  1. Unit tests for small deterministic components.
  2. Integration tests for full estimation pathways.
  3. Regression tests for historical bug scenarios.

Each layer catches different failure modes.

Make Failures Actionable

A failing test should point to a likely diagnosis. Include structured failure messages and context fields such as:

  1. Scenario identifier.
  2. Seed and configuration.
  3. Metric value vs threshold.

Actionable failures reduce debugging time and improve confidence in fixes.

Key Takeaway

Deterministic testing is compatible with stochastic methods when reproducibility controls and tolerance policies are explicit.