Sampling and uncertainty

geoestimate assumes that rows have equal inclusion probabilities. The declaration Sample(data, cluster="itinerary_id") changes the uncertainty calculation, not the point estimate.

Design-based default

inference="design" follows the declared design:

• Without a cluster, it reports an iid standard error and normal interval.

• With a cluster, it sums linearized contributions within clusters, applies the finite-cluster correction, and reports a Student-t interval with G - 1 degrees of freedom.

Clusters must be independent sampling or collection units. Spatial proximity or outcome correlation alone does not decide whether clustering is appropriate.

Bootstrap

inference="bootstrap" resamples the design units. It resamples individual rows for an iid sample and whole clusters for a clustered sample.

result = sample.ratio(
    "n_women",
    "n_people",
    inference="bootstrap",
    bootstrap_reps=2_000,
    seed=42,
)

The result contains the standard deviation of the bootstrap estimates and a percentile confidence interval. Ratio resamples with a zero denominator total are undefined. The implementation redraws them up to a fixed attempt limit and fails if the sample contains too little positive denominator information.

Diagnostics

Every result contains iid and, when declared, clustered analytic standard errors. The design effect is the clustered variance divided by the iid variance. The effective sample size divides the observation count by that design effect. These are descriptive diagnostics, not corrections for an unsupported sampling design.