Estimate multilevel metric generalizability

What it's for

Stops dashboards and AI agents from comparing teams, repositories, or projects using a metric whose measurement design cannot support the decision.

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": 200,
  "observations": [
    {
      "id": "reliability-0-0",
      "period_id": "week-0",
      "unit_id": "repository-0",
      "value": 0
    },
    {
      "id": "reliability-0-1",
      "period_id": "week-1",
      "unit_id": "repository-0",
      "value": 0.05
    },
    {
      "id": "reliability-0-2",
      "period_id": "week-2",
      "unit_id": "repository-0",
      "value": 0.1
    },
    {
      "id": "reliability-0-3",
      "period_id": "week-3",
      "unit_id": "repository-0",
      "value": 0.15000000000000002
    },
    {
      "id": "reliability-0-4",
      "period_id": "week-4",
      "unit_id": "repository-0",
      "value": 0.2
    },
    {
      "id": "reliability-0-5",
      "period_id": "week-5",
      "unit_id": "repository-0",
      "value": 0.25
    },
    {
      "id": "reliability-0-6",
      "period_id": "week-6",
      "unit_id": "repository-0",
      "value": 0.30000000000000004

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.

{
  "assumptions": [
    "Units and periods represent the intended comparison universe, the metric definition is stable, and missing observations are ignorable after the declared design.",
    "The crossed additive unit/period decomposition is adequate; unit-by-period interactions and autocorrelation are absorbed into residual variance.",
    "Method-of-moments corrections approximate an unbalanced generalizability study and cluster bootstrap resamples whole units; highly sparse designs need a full mixed-effects model.",
    "Dependability qualifies a privacy-eligible group metric for comparison and is not evidence of individual effort, intent, or employment suitability."
  ],
  "coefficients": {
    "absolute_dependability": 0.999984,
    "absolute_interval": [
      0.999971,
      0.999987
    ],
    "planned_observations_per_unit": 10,
    "relative_generalizability": 1,
    "relative_interval": [
      1,
      1
    ],
    "required_observations_per_unit": 1,
    "target_dependability": 0.8
  },
  "decision": "metric_dependable_for_group_comparison",
  "method": "crossed_unit_period_generalizability_mom_cluster_bootstrap_v1",
  "sample": {
    "bootstrap_draws": 200,
    "confidence_level": 0.95,
    "minimum_observations_per_unit": 10,
    "observations": 200,
    "periods": 10,
    "units": 20
  },
  "variance_components": {
    "period": 0.02294911,
    "residual": 0.00002573,
    "unit": 140.0066484,
    "unit_share": 0.999836
  }
}

How it works

Forecasting & survival — Estimate when something completes or fails, with censoring and unresolved work handled honestly rather than dropped.

1. 1 Decompose aggregate management-metric variance into unit, period, and residual components, bootstrap reliability, and calculate the sampling needed for dependable comparisons.
2. 2 Evaluate the method-specific diagnostics and gates returned by the function, then abstain unless the declared decision clears them under locally governed thresholds.

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": "decompose aggregate managementmetric variance into unit" }
  → finds "estimate_multilevel_metric_generalizability"

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

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

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

Related tools

See every tool in Measurement integrity →