autoresearch

Structured hypothesis testing requires defining a metric, a surface, a measurement window, and an evaluation method. You then form testable hypotheses, apply measured changes, and evaluate outcomes to decide whether to keep or discard the changes based on tracked results.

An autoresearch cycle formalizes iterative experimentation by planning, executing, and evaluating experiments tied to a specific metric and surface. It applies measurement-driven decisions to track results over defined windows and learn from experimental outcomes.

To set up an experimentation loop you need a chosen metric, a surface, a measurement window, and a defined evaluation method. These inputs allow you to create, run, and evaluate experiments while maintaining audit trails for structured learning.

Defining decision criteria involves establishing a clear evaluation method before running the experiment. You track results over the defined measurement window and use the predetermined criteria to decide whether to keep or discard the applied changes.

Yes, this structured experimentation approach supports creating, running, and evaluating experiments with built-in audit trails. This ensures documented measurement-driven decisions and hypothesis-driven learning across your data workflows.

The best way to formalize iterative experimentation is through a structured loop of planning, executing, and evaluating changes tied to a metric. This ensures hypothesis-driven learning and prevents unstructured changes by requiring clear decision criteria.