Define the concept before you verify anything
A “verification problem” is a situation where information claims are hard to confirm because definitions, data, or procedures are unclear or inconsistent. Verification can fail when people mix up what is being claimed (the statement) with what determines the outcome (the inputs and conditions).
Start by rewriting the claim in a testable form:
- What exactly is being asserted (e.g., a metric, a causal explanation, a procedure outcome)?
- What inputs would need to be true for the claim to hold?
- What would count as evidence for and against it?
Build a source hierarchy you can explain
Use a hierarchy of sources so that you can justify why some information is more verifiable than other information.
-
Primary or authoritative descriptions Use direct, official material that defines terms and processes (for example, regulatory rules, central bank publications, official statistics, or the documented methodology of the system you are studying). If a claim depends on a moving target (policy, availability, or rules), the “right” source is the most current official text, not a secondary summary.
-
Methodological explanations When primary material is technical, rely on independent explanations that focus on method: how measurements are defined, what assumptions are needed, and how results should be interpreted.
-
Secondary interpretations Treat commentary and summaries as hypotheses until they can be traced back to definitional documents or reproducible procedures.
Make verification reproducible with explicit assumptions
Verification steps should be replicable by a reader without special context. Use the same inputs, units, and definitions.
A reproducible example usually includes:
- Inputs: the data fields or parameters used.
- Assumptions: what is assumed constant (for example, fixed fees, execution cost model, or timing).
- Computation method: the exact procedure and formulas, or a documented algorithm.
- Rounding and checks: how you handle decimals and whether results are consistent under small changes.
Then run “sanity tests” by changing one input at a time. If the conclusion only appears under one very specific setup, that is a warning sign.
Separate stable mechanics from variable conditions
Some parts of an explanation are generally stable (definitions, measurement logic, accounting for costs). Other parts vary (conditions, execution details, and the specific environment where the information is produced).
A good verification practice is to tag each element:
- Stable mechanics: can be explained without time-sensitive details.
- Variable conditions: depend on context you must independently confirm.
If a source blends these together, you cannot reliably verify the claim, because changing conditions could create a different outcome even when the stable mechanics are correct.
Identify at least one failure mode
Verification problems commonly fail due to predictable issues:
- Definition mismatch: the claim uses one meaning of a term while the evidence uses another.
- Missing costs or frictions: results omit relevant components (for example, fees or execution-related effects), creating misleading comparisons.
- Hidden assumptions: a procedure depends on timing, sampling, or system behavior that is not stated.
- Overgeneralization: historical relationships are treated as if they must apply in the future.
Any verification attempt should state which failure mode you are guarding against.
Verification limitations and what to check next
Verification does not guarantee correctness; it reduces uncertainty. Outcomes vary with conditions, costs, and execution details, and historical relationships do not establish future results. To verify the “relevant facts,” focus on whether the definitions, inputs, and procedure are fully specified and whether your checks still hold under reasonable alternative inputs.
Next, ask:
- Can another reader reproduce your procedure with the same stated assumptions?
- Do you have a clear match between the claim and the evidence definitions?
- What is the smallest change that would invalidate the claim?
If the answer is “unknown,” the verification problem is not solved yet; it needs clearer definitions, more complete assumptions, or more authoritative source material.