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Module 02 / Lesson 06 · Read, apply, check
Build defensible evidence that makes a client story useful and difficult to imitate.
Write the proposed claim as a question before collecting data. “Did the new onboarding process reduce delays for the pilot group?” is more useful than searching every spreadsheet for an impressive percentage. Define the population, period, measure and comparison. Ask what could disprove the claim and what information is unavailable. AI can organize supplied records and suggest checks, but it cannot create a real customer outcome that was never measured. The best evidence collection plan is small enough to complete and precise enough to answer the editorial question.
Record how the data was gathered, who is included, what was excluded and whether definitions changed. A comparison of average response time is misleading if the new period excludes difficult cases that the old period included. Retain the underlying approved records and calculation steps. Distinguish counts, rates, medians and averages, and label the units. For a small or self-selected sample, describe its limits directly. A modest result with a transparent method can be more useful than an impressive headline that cannot survive a basic follow-up question.
A customer example can show how a process affected daily work, but one enthusiastic account does not prove every customer had the same experience. Ask what changed, what remained difficult and what other factors contributed. Obtain the appropriate permission for names, quotations and identifying details. Pair an outcome figure with a concrete sequence of events and an available spokesperson. When customer permission is unavailable, use a genuinely anonymized description only if the agreed terms allow it and the details do not identify the person indirectly.
For each item, record a source ID, owner, date, method, permitted use and the strongest claim it supports. Add a limitation field and any contradictory evidence. Keep raw records separate from working analysis and approved public extracts. The editor should be able to trace a sentence back to an exact source without asking the original researcher to reconstruct the project. Before briefing AI, remove irrelevant personal or confidential information and include only what is authorized for the intended workflow and audience.
Define population, period, measure and comparison.
Keep source IDs, method and exclusions attached.
Add firsthand examples with context and permission.
State only what the evidence supports, including limits.
A fictional software client believes its onboarding redesign improved activation. The agency asks for a defined pilot rather than a company-wide success claim. The client supplies counts for forty eligible accounts before the change and forty after, with a consistent activation definition and period. The editor notices that one group included assisted onboarding while the other did not. The team separates those cases and narrows the comparison. Two customers explain which instructions were clearer and where support was still needed. Their comments are recorded with permission and linked to source IDs. The final evidence pack supports a bounded story about the pilot, includes the group difference and avoids claiming that the redesign alone caused every improvement. AI can now help draft from a coherent factual base.
Another agency searches a large export for the highest percentage change and builds the headline around it. The selected metric covers a different period, omits unsuccessful accounts and has no named owner. A customer quote is added from an old sales deck without checking permission. The resulting article looks data-rich, but its evidence cannot support the story. More charts and larger numbers have made the underlying uncertainty harder to see.
Original data is not automatically strong data. It may be incomplete, biased or unsuitable for the question. Preserve the distinction between an observed change, a plausible explanation and a demonstrated cause. Do not ask AI to “prove” the desired narrative; ask it to identify what the records support, what they do not and which checks would change the conclusion.
Customer and employee records may contain confidential or personal information. Use approved extracts and appropriate access controls, and involve the client’s qualified reviewer where needed. This lesson teaches evidence organization, not a substitute for specialist research design or legal advice on data use and disclosure.
Do this today · 30 minutes
Design an evidence request for the onboarding pilot.
A document or spreadsheet and the illustrative case in this lesson. Use synthetic or already approved material for practice; no paid account or real client data is required.
Before: Define population, period, measure and comparison.
After: State only what the evidence supports, including limits.
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Test this worksheet on one bounded assignment. Record the reviewer’s findings, revise the method and assign the next review date.
Before you move on
A client reports a 60% improvement from a pilot, but the before-and-after groups use different eligibility rules. The agency wants the number in the headline because it is original client data. What is the best editorial response before drafting?
Next lesson →Associated Press: newsroom standards for AI
PRSA: professional ethics resources
Lesson edition 1.0 · References reviewed 16 October 2026 · Practical exercise reviewed 16 October 2026. Examples are illustrative teaching scenarios.
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