Audit analytics challenger independence

What it's for

Shows leaders whether a second analytical opinion actually catches the first system's failures, rather than merely duplicating its blind spots behind another brand or model.

What you give it

Inputs split into evidence read from your connected systems, calibration your team owns, and numerical controls that affect precision but never the result's meaning.

{
  "bootstrap_draws": 300,
  "confidence_level": 0.8,
  "observations": [
    {
      "challenger_error": -9,
      "challenger_failed": true,
      "challenger_id": "independent-reviewer",
      "decision_period": 0,
      "id": "challenge-pair-000",
      "incumbent_error": -8,
      "incumbent_failed": true,
      "weight": 1
    },
    {
      "challenger_error": 2,
      "challenger_failed": false,
      "challenger_id": "independent-reviewer",
      "decision_period": 1,
      "id": "challenge-pair-001",
      "incumbent_error": -1,
      "incumbent_failed": false,
      "weight": 1
    },
    {
      "challenger_error": -6,
      "challenger_failed": false,
      "challenger_id": "independent-reviewer",
      "decision_period": 2,
      "id": "challenge-pair-002",
      "incumbent_error": 6,
      "incumbent_failed": false,
      "weight": 1
    },
    {
      "challenger_error": 5,
      "challenger_failed": false,
      "challenger_id": "independent-reviewer",
      "decision_period": 3,
      "id": "challenge-pair-003",
      "incumbent_error": -4,
      "incumbent_failed": false,
      "weight": 1
    },

What you get back

This is the actual output of running the example above — computed by the same function the platform calls, not an illustration.

{
  "challenger_diagnostics": [
    {
      "absolute_error_correlation_upper_bound": 0.3025,
      "challenger_id": "independent-reviewer",
      "decision": "independent_challenge_supported",
      "failed_gates": [],
      "incumbent_failure_catch_rate": 0.8333,
      "incumbent_failure_catch_rate_lower_bound": 0.6667,
      "incumbent_failure_count": 12,
      "joint_failure_rate": 0.0333,
      "joint_failure_rate_upper_bound": 0.0667,
      "observation_count": 60,
      "weighted_error_correlation": -0.0144
    }
  ],
  "configuration": {
    "block_length": 5,
    "bonferroni_one_sided_tail_probability": 0.0667,
    "bootstrap_draws": 300,
    "confidence_level": 0.8,
    "maximum_absolute_error_correlation": 0.7,
    "maximum_joint_failure_rate": 0.2,
    "minimum_incumbent_failure_catch_rate": 0.5,
    "minimum_incumbent_failure_count": 10,
    "minimum_observation_count": 30,
    "seed": 17,
    "simultaneous_gate_count": 3
  },
  "decision": "independent_challengers_available",
  "guardrails": [
    "A challenger is independent only relative to the represented loss, decisions, epoch and failure definition. Different code, vendor or model family is not evidence of independent errors.",
    "Block bootstrap preserves short temporal dependence and Bonferroni-adjusts the three gates across every screened challenger; it does not repair selective challenge, missing outcomes or a shared omitted data defect.",
    "Support justifies governed dual-review design, not automatic override. Challenger comparisons concern analytical systems and must never become individual performance or surveillance scores."
  ],
  "method": "paired_temporal_block_bootstrap_challenger_independence_v1",
  "summary": {
    "challenger_count": 1,
    "supported_challenger_count": 1,
    "supported_challenger_ids": [
      "independent-reviewer"
    ]
  },
  "truncation": {

How it works

Statistical audit & measurement — Check whether a number is fit to decide on: coverage, timing, reconciliation, and the gaps a dashboard hides.

1. 1 Freeze one prospective decision cohort, incumbent and challenger versions, signed error measure, material-failure definition, outcome horizon and observation weights before inspecting comparative results.
2. 2 Within each challenger, order paired observations by decision period, compute weighted error correlation, conditional incumbent-failure catch rate and joint failure rate, then moving-block bootstrap whole temporal stretches.
3. 3 Bonferroni-adjust all three one-sided bootstrap gates across the full screened challenger family, then support independence only when every conservative bound clears locally governed limits and minimum observation, failure and error-variation evidence is present.

Before you trust it

Every tool in the catalog ships with the conditions under which its answer is meaningful — and the conditions under which it should abstain instead of guessing.

Calibrating it to your org

Calibration workflow

1. 1 Define the management decision, target outcome, aggregate unit, privacy boundary, cadence, and prediction/intervention horizon for this organization.
2. 2 Build a tenant-scoped historical cohort using only information available before each prediction or decision; preserve zero periods, censoring, assignment probabilities, and unresolved outcomes when the method requires them.
3. 3 Estimate statistical parameters on training history, but obtain costs, utilities, risk tolerance, practical-effect thresholds, capacity, and policy constraints from accountable decision owners.
4. 4 Validate on later time windows or held-out aggregate units at the deployment grain, against a simple baseline and the function-specific validation strategy.
5. 5 Deploy only if the returned decision clears evidence, overlap, calibration, robustness, and guardrail checks; warning, unsupported, schema-gap, and fallback decisions are abstentions.
6. 6 Monitor realized outcomes, data drift, coverage, and decision regret; recalibrate at a governed cadence or after a detected regime/definition change, never merely because a stakeholder dislikes the result.

Call it from your AI

You don't wire up 388 tools in your MCP client. The GitRevio MCP server exposes 18 tools, three of which let an agent search the catalog, read a tool's schema, and run it — so the assistant finds this one on its own.

gitrevio_capabilities_search
  { "q": "audit whether an analytical challenger supplies" }
  → finds "audit_analytics_challenger_independence"

gitrevio_capability_describe
  { "capability_id": "audit_analytics_challenger_independence" }
  → returns the input schema and agent guidance shown on this page

gitrevio_capability_run
  { "capability_id": "audit_analytics_challenger_independence", "arguments": { ... } }
  → returns the result shown above

Works in Claude Desktop, Claude Code, Cursor, Cline, Continue.dev, Goose and Aider. See the MCP server.

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