Developing and evaluating methods

Test a method against the quantity it is meant to estimate

A method can be fast, stable, and precise while answering the wrong question. A useful evaluation starts with a target defined independently of the competing procedures, then asks what each procedure estimates and which evidence supports its claims.

Simulation supplies known population truth; sensitivity analysis probes assumptions or choices that observed data may not settle. Both need a clear protocol. Discovery counts or agreement between methods alone do not establish accuracy, calibration, or efficiency.

Two questions organize this guide. Does the procedure support its claim for the stated target? Answer with its limit, finite-sample errors, uncertainty calibration, and computational reliability. Which assumptions make that conclusion possible? Use controlled simulation departures and explicit sensitivity parameters to distinguish failures of estimation from limits of what the observed data identify.

What you’ll learn

• Build a fair evaluation: define a common target, choose relevant performance measures, compare methods on shared simulated datasets, quantify Monte Carlo uncertainty, and report failed attempts.
• Explain the scope of the conclusion: distinguish sampling error, target bias, and numerical problems; then probe identification assumptions while keeping the population and measurement target explicit.

The checkpoints give completion targets for each page. Additional practice extends the calculations and applications; it is optional. The group’s published manuscripts supply research case studies, while these pages use controlled teaching examples to make the evaluation arguments inspectable.