Setting Bases

A setting basis is where the value in a parameter came from: a public standard that fixes it, a benchmark that supplies it, an experiment that established it, or a decision that chose it. A setting names its basis so a reader knows how far to trust the number and what would change it.

What you can do with a setting basis

• tableEvidence Plan · Settings

Its setting bases— 4

• StandardA public standard fixes the value and the business adopts it as written: the share of pixels and the time an impression must stay in view.
• BenchmarkA published benchmark or a platform default supplies the value, and the business takes it until it has evidence of its own.
• ExperimentAn evidence design established the value: a holdout, a geo experiment or a model calibrated it, and the result is what stands.
• DecisionThe business chose the value, stated it and owns it: a lookback window, a frequency cap, a retention period.

Why it matters

What goes wrong without it, as the commitments that license this kind say it, each standing on its sources.

• A value a plan fixes is a governed parameterA plan that runs on numbers nobody declared runs on numbers nobody can trace: a lookback window set in one platform's defaults, a frequency cap in another's, a visit timeout in a third's and a lapse rule in a spreadsheet, each changing a reading without changing its name. The standards bodies fix some of these values outright, the experimentation literature says the design parameters are set before a test and never after, and the regulation requires a retention period without naming one. Declaring the slot as vocabulary and the value as a record with its basis is what lets a business supply its own context without touching the model, and what lets an export carry the value a computation actually needs. The private platform this model learned from wrote the same rule, that a value store never defines meaning, and it rotted where the slots were named for the engine rather than the marketing and the citations were never made.
  Viewable Ad Impression Measurement Guidelines, Version 2.0 · Outcomes and Data Quality Standards · Digital Audience-Based Measurement Standards · Trustworthy Online Controlled Experiments: A Practical Guide to A/B Testing · The Unfavorable Economics of Measuring the Returns to Advertising · A Comparison of Approaches to Advertising Measurement: Evidence from Big Field Experiments at Facebook · Regulation (EU) 2016/679, Article 5: Principles relating to processing of personal data · ISO 80000-1:2022 Quantities and units, Part 1: General