GRC › Module 7 › Lesson 3
Sampling Named
Audit sampling literacy — population definition, sample size stub, selection method, exception threshold, extrapolation note — named sampling rows on YOUR `$GRC_LAB` audit workpapers.
Visual · t37_sampling_named
Sampling = named audit method rows. $GRC_LAB. Original Cyberlium.
Opening
Auditors sample populations — name selection method and size rows on YOUR lab workpapers before cherry-picking samples to hide gaps or forge clean audit results.
Audit sampling literacy names: population definition category, sample size methodology stub category, random vs judgmental selection category, exception rate threshold category, and extrapolation to population note category. Analyst documents sampling plan on `$GRC_LAB` internal audit workpaper — population from lab control catalog, sample size literacy stub, selection method — without cherry-picking only passing samples to forge clean results, without hiding exceptions from external auditors, without sampling stranger org populations without authorization. Cyberlium teaches sampling vocabulary on YOUR notes. Refused: cherry-picked samples, hidden exceptions, forged sampling documentation. Lab row: sampling plan (population, size stub, method, threshold, LAB label).
1. Named sampling rows
Population, sample size, selection method, exception threshold, extrapolation — five anchors.
Population cites `$GRC_LAB` control test universe — not stranger org data.
Command guide
Try these commands — Named sampling rows
═══ TOOLS & WEBSITES ═══ Browse / read these (authorized learning only — stay in YOUR lab / program scope)
ISACA sampling — https://www.isaca.org/resources/glossary ISO 19011 audit guidelines — https://www.iso.org/standard/75106.html NIST CSF — https://www.nist.gov/cyberframework
═══ INSTALL ═══
Linux (Debian/Ubuntu):
Command — copy this
sudo apt install python3
macOS:
Command — copy this
brew install python3
Windows: Download https://python.org/downloads/
═══ LINUX / macOS ═══
Command — copy this
export GRC_LAB=${GRC_LAB:-$HOME/cyberlium-lab/t37-grc}
cat > "$GRC_LAB/notes/audit-sampling-named.txt" <<'EOF'
Audit sampling — NAMED LITERACY:
Population: all items in scope (e.g. all privileged users this quarter)
Sample size: risk-based — higher risk = larger sample
Methods: random, stratified, judgmental (document rationale)
Exception: sample item failed control → expand sample or project failure
Workpaper: list sample IDs tested (fictional U-001..U-005 in lab)
Lab example: test 5 of 50 admin accounts for MFA enforcement
EOFCommand — copy this
grep -E 'Population|Sample size|Exception|MFA' "$GRC_LAB/notes/audit-sampling-named.txt"
python3 -c "print('Sampling: document population, method, exceptions — literacy only')"Primary tools to practice this lesson: grep, python3. Reference sites: ISACA sampling (https://www.isaca.org/resources/glossary); ISO 19011 audit guidelines (https://www.iso.org/standard/75106.html); NIST CSF (https://www.nist.gov/cyberframework). Run every command in the box — install first, then the usage lines — only on YOUR lab / program scope.
2. Selection discipline
Random selection literacy preferred over judgmental cherry-pick stub.
Exception threshold documented before testing — not moved after results.
3. Refused
No cherry-picked samples; no hidden exceptions; no forged clean sampling results.
Sampling literacy supports honest audit — not result manipulation.
4. What you ship: sampling plan
Population + size stub + selection method + threshold + NEVER cherry-pick line.
5. What you record before the next lesson
Sampling plan path.
6. Wrong vs right: fraudulent certs vs YOUR lab templates
Worked failure — same MSF word, opposite target. Right never needs a café Wi-Fi or classmate laptop.
Wrong
Cherry-pick only passing `$GRC_LAB` samples and hide exceptions to forge clean audit report.
Right
Sampling plan from `$GRC_LAB` workpaper. Next: Audit Lab.
Mission: sampling plan
1) Name five sampling literacy rows. 2) Define population from lab catalog. 3) Sample size and selection method stub. 4) Write NEVER cherry-pick sample line.
Stuck? Ask Cyberlium AI Mentor
Ask Mentor: “Random vs judgmental — literacy when to use?”
Knowledge Check
APPLY: Sampling literacy uses:
Multiple choice
Knowledge Check
APPLY: True or False: Cherry-picking samples to hide gaps is acceptable.
True or False
Knowledge Check
APPLY: Sampling plan includes:
Multiple choice