Context Makes Evidence Meaningful

“Conversion increased 25%” sounds strong.

Without baseline, timeframe, traffic source, or attribution, its real meaning is unclear.

Polished screenshots, positive testimonials, large metrics, and badges can create an impression.

Deep trust requires interpretability.

Evidence — what signal is being shown?

Context — what are the scope, baseline, timeframe, role, and constraints?

Interpretation — what does the evidence actually indicate?

Relevance — how closely does it support the current claim?

Trust — can the reader evaluate it fairly?

A move from 2% to 3% and 20% to 30% are both +50%, but their business meaning differs dramatically.

A one-week spike is different from a six-month trend.

Timeframe helps readers judge durability and representativeness.

A team result is not automatically an individual contribution.

Context should clarify who did what and what other factors influenced the outcome.

The same result under a small budget, tight deadline, or limited data can reveal very different judgment than in ideal conditions.

A famous case in a different context may be less persuasive than a smaller case close to the current problem.

Forwork can attach a Context Card to evidence: Project, Role, Scope, Baseline, Timeframe, Constraints, Source, Attribution.

This helps both people and AI interpret proof correctly.

Do not only ask, “Is this evidence strong?”

Ask: “Does the reader have enough context to interpret this evidence correctly?”

Evidence → Context → Interpretation → Relevance → Trust.