Assumptions and caveats log#

This log contains a list of assumptions and caveats used in this analysis.

Definitions#

Assumptions are red, amber or green (RAG) rated according to the following definitions for quality and impact[1]:

RAG rating

Assumption quality

Assumption impact

GREEN

Reliable assumption, well understood and/or documented; anything up to a validated & recent set of actual data.

Marginal assumptions; their changes have no or limited impact on the outputs.

AMBER

Some evidence to support the assumption; may vary from a source with poor methodology to a good source that is a few years old.

Assumptions with a relevant, even if not critical, impact on the outputs.

RED

Little evidence to support the assumption; may vary from an opinion to a limited data source with poor methodology.

Core assumptions of the analysis; the output would be drastically affected by their change.

Assumptions and caveats#

Assumptions and caveats last updated: 16/05/2023

This analysis contains the following assumptions and caveats:

Assumption 1: Title of assumption#

  • Location: source/example_assumptions.py

  • Quality: RED

  • Impact: AMBER

Detailed description on next line or many. Assumption: Another assumption Q: GREEN I: RED Leaving an empty newline after the previous one.

Q and I can be used for shorthand RAG ratings.

Assumption 2: Yet another assumption#

  • Location: source/example_assumptions.py

  • Quality: RED

  • Impact: GREEN

Indented? No problem.

Caveat 1: Oh oh#

Location: source/example_caveats.py

Something is not as it seems