FTB-C000310 / Data quality concept

Startup Bias

Startup Bias is the distortion in early recursive outputs caused by limited history and the selected initial state.

Also known asinitialization bias

Intermediate 6 linked topicsPublished

Definitions

In plain terms

Different EMA or adaptive-filter seeds can create different early paths even with identical later inputs.

Technical

The contract records seed rule, first-ready point, prehistory, warm-up disclosure, and convergence or exclusion policy.

Scope

Discarding a few rows does not prove startup bias has vanished for every parameter.

Examples

  • A governed lesson uses Startup Bias only with declared inputs, timing, parameters, and edge-case behavior.

Common misconceptions

  • Discarding a few rows does not prove startup bias has vanished for every parameter.

Concept relationships

Prerequisites

Related

Where this concept is used

Tutorials planned

These catalogued topics use this concept, but their complete build has not shipped yet.

  • D07-F01-A02 Important
  • D07-F01-A04 Important
  • D07-F01-A05 Important
  • D07-F01-A06 Important
  • D07-F01-A08 Important
  • D07-F01-A09 Important

Evidence and governance

  1. Moving Average and Smoothing Methods NIST/SEMATECH · official standard

    Supports: preferred label, short definition, technical definition

    Limits: Forecasting notation and initialization can differ from technical-indicator conventions and must be translated explicitly.

Reviewed by
fintech-builder-batch-006
Last reviewed
2026-07-27
Next review
2027-07-27
Record status
published
Written by

Fintech engineer building market-data and financial systems, and the author of every article, glossary record, and reference implementation on The Fintech Builder.