# The Most Common Test Evidence Mistakes and How to Fix Them

> Many validation tests are executed correctly, but evidence is weak. Weak evidence creates audit risk because reviewers cannot reconstruct what

- Author: Tomaž Berden (https://lifescienceai.org/authors/tomaz-berden/)
- Published: 2026-07-01
- Category: Fundamentals
- Canonical URL: https://lifescienceai.org/articles/the-most-common-test-evidence-mistakes-and-how-to-fix-them/
- Word count: 195
- Platforms named: Qualitum (https://qualitum.ai/)

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## Evidence fails for predictable reasons

Many validation tests are executed correctly, but evidence is weak. Weak evidence creates audit risk because reviewers cannot reconstruct what happened.

## The top mistakes

Vague results. “Pass” with no proof of what was verified.

Missing context. No roles, no test data, no environment details.

Scattered attachments. Screenshots and logs stored inconsistently.

No traceability. Requirement links missing or unclear.

No review proof. Tests executed but not reviewed and approved per SOP.

## How to fix fast

Standardize a test template, require evidence links, and maintain an evidence index. Evidence quality improves immediately when structure is consistent.

## How this looks with an agentic layer

The fundamentals above do not change when validation is automated - they get enforced earlier. On a layer like Qualitum, intended use, risk and evidence are structured inputs rather than documents someone remembers to write: agents draft against them, maintain the traceability, and hand the result to a named human for approval.

- Intended use and critical data are captured once and reused across the lifecycle
- Evidence is generated with its traceability attached, not reconstructed for the audit
- The approval - and the accountability - stays with your named reviewer

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Source: LifeScienceAI - https://lifescienceai.org/articles/the-most-common-test-evidence-mistakes-and-how-to-fix-them/. Editorial analysis, not regulatory advice. Cite as: Tomaž Berden, "The Most Common Test Evidence Mistakes and How to Fix Them", LifeScienceAI, July 1, 2026.