Last Updated on September 25, 2025 by Melissa Lazaro

Statistical Records Management for ISO/IEC 17043:2023 Compliance

Let’s get real—statistical records often get treated as “back-end” files. They’re created, analyzed, and stored, but rarely revisited until audit season rolls around. And that’s where the trouble starts.

Under ISO/IEC 17043:2023, your statistical records aren’t just supporting documents—they’re essential evidence that your PT schemes are scientifically valid, traceable, and fair. If your statistical data is disorganized, untraceable, or lacks transparency, assessors will spot it.

In this article, I’ll break down how to manage statistical records the right way—from raw data to final scoring reports—so you can stay compliant and confidently defend your results when the time comes.

What ISO/IEC 17043:2023 Expects for Statistical Data

ISO/IEC 17043:2023 doesn’t spell out every statistical detail, but it’s clear on one thing: documented information is essential for:

• Homogeneity and stability testing
• Participant performance evaluation
• Final PT reporting
• Management system effectiveness (Clause 8.5)

You need to be able to show how you calculated scores, who ran the analysis, and what data supports your conclusions. Not just once—but every time.

Types of Statistical Records You Must Maintain

Here’s a breakdown of the core statistical records most PT providers are expected to keep.

Homogeneity and Stability Testing

You must document:

• Raw measurements from testing
• Statistical summaries (means, SDs, ANOVA results)
• Graphs or charts (e.g., control charts or box plots)
• Acceptance criteria (and justification)

Example: A PT provider tested ten units of a test material. The raw data, mean and standard deviation, and F-test results were all stored in an Excel file—with the summary copied into the final scheme validation record.

Performance Evaluation

This is the meat of your statistical analysis and where things often go sideways.

You need:

• All participant results
• Assigned values and their traceability
• Calculation of z-scores, robust SDs, MADs
• Outlier detection logs
• Uncertainty estimates, if included

Example: You issued z-scores based on a robust mean and SD. Make sure the worksheet (or script) that generated those values is archived and labeled with the PT round, date, and analyst initials.

Scheme Design and Validation

Sometimes overlooked, these records show how your scheme was built.

• Historical data or simulations used for planning
• Statistical assumptions
• Validation summary of how you confirmed the scheme was fit-for-purpose

Participant Reporting and Feedback

You should also retain:

• Draft and final participant reports
• Records of feedback or result disputes
• Any communication logs related to scoring, result review, or appeals

Even if this overlaps with your customer service records, you must be able to trace it back to statistical outcomes.

How to Store and Organize Statistical Records

Whether you’re using spreadsheets, statistical software (like R or SPSS), or built-in LIMS features, what matters is organization and integrity.

Here’s what I recommend:

• Centralized folder structure: By scheme, year, and PT round
• Clear file naming: Include date, scheme code, and revision
• Separate raw data from processed outputs
• Backups: Schedule regular backups, especially for formula-based files
• Linkage to PT scheme documentation: e.g., sample IDs, participant codes

Pro tip: Always store both raw data files and processed result files—don’t overwrite your originals.

Ensuring Integrity and Reproducibility

Here’s the part many labs miss. It’s not enough to store data—you have to show how it was processed.

What that means in practice:

• Keep calculation worksheets or code with clearly labeled steps
• Log software used, including version (especially if it’s a homegrown Excel template or R script)
• Note who performed the analysis and when
• Add comments or footnotes if a decision was made to remove outliers or adjust evaluation criteria

This creates what I call an “Analysis Audit Trail”—a simple one-pager linking all your statistical files for each round. It’s gold during audits.

Pro Tips

• Pro Tip: Build a one-page “Analysis Audit Trail” template that summarizes file names, dates, analysts, and methods used for each PT round.
• Pro Tip: Version-control your spreadsheet templates. If scoring methods change, make sure it’s documented.
• Pro Tip: Use locked Excel cells or protected PDF exports to prevent accidental changes to final analysis outputs.
• Pro Tip: If using code (R, Python), keep it in a dedicated repository and require comments in every script that explain what it does.

Common Mistakes to Avoid

Mistake #1: Overwriting Previous Rounds’ Files

It’s tempting to reuse templates—but always save as new and archive the original.

Mistake #2: Using Complex Macros with No Documentation

Assessors (and even your future self) need to understand how the math was done.

Mistake #3: Poor File Naming

“Data1.xlsx” and “Results_Final_NEW2.xlsx” won’t cut it. Use naming that links to the PT round and date.

Mistake #4: No Link Between Submissions and Scoring

If you can’t trace each participant’s raw submission to their score, you’re risking a serious nonconformity.

FAQs

Q: Can we outsource the statistical work?
Yes—but you’re still responsible for retaining the data and showing that the subcontractor is competent. That includes verifying methods, results, and records.

Q: Are Excel files acceptable for scoring?
Yes, if they’re version-controlled and formulas are transparent. Avoid hidden cells or locked sheets that no one can explain.

Q: How long should we keep statistical records?
Typically 5–7 years, or longer if your retention policy or accreditation body requires it. Be consistent, and document your justification.

Make Your Numbers Defensible

ISO/IEC 17043:2023 puts heavy emphasis on results that are reliable, traceable, and reproducible. Your statistical records are the backbone of that trust.

The most successful PT providers I’ve supported treat their statistical data like intellectual property—organized, backed up, and ready for review at any time.

Want to simplify your process? I can send you a ready-to-use Statistical Records Checklist or an “Analysis Audit Trail” template to help get you started.