Evidence Verification · 6 May 2026 · 7 min read
Separating Correlation From Verifiable Evidence
By Digital Asset Claims Research Desk·Investigation Team
- Evidence Standards
- Correlation
- Investigative Method
Large timestamped datasets, of the kind generated by any digital asset investigation, are fertile ground for apparent correlations. This article explains why correlation is treated as a starting hypothesis rather than a conclusion, and how it is tested before being accepted as a finding.
Two events occurring close together in time is a pattern worth investigating. It is not, by itself, a conclusion about their relationship.
Why Correlation Is Easy to Find
Investigations generate large volumes of timestamped data across many sources, and with sufficient volume, apparent patterns will emerge even where no genuine relationship exists.
Analysts working intensively on a single case can develop a heightened sensitivity to patterns that confirm their working theory, which increases the risk of treating a coincidental correlation as significant.
Recognising this tendency is the first step in guarding against it, which is why structured review processes exist specifically to challenge early hypotheses rather than confirm them.
Testing Alternative Explanations
Before accepting a correlation as meaningful, analysts actively construct and test alternative explanations, such as shared use of a popular service that would produce a similar pattern by coincidence.
Where an alternative explanation is at least as plausible as the original hypothesis, the correlation is documented as unresolved rather than treated as supporting the original theory.
This step often requires additional data gathering specifically aimed at distinguishing between competing explanations, rather than simply accumulating more evidence in favour of the initial hypothesis.
Base Rates and Statistical Context
Understanding how often a given pattern would be expected to occur by chance, given the overall volume of activity involved, is essential to judging whether an observed instance is genuinely notable.
A pattern that seems striking in isolation may be unremarkable once its base rate is considered, particularly on networks with very high transaction volumes and widely used intermediary services.
Where base rate data is not readily available, analysts document this limitation explicitly rather than presenting an untested correlation with unwarranted confidence.
From Correlation to Documented Finding
A correlation is elevated to a documented finding only once it has been tested against alternative explanations and corroborated through at least one genuinely independent additional source.
The language used in the final report reflects this process precisely, distinguishing between associations that remain provisional and conclusions that have survived independent verification.
This calibrated language is not a hedge; it is an accurate representation of what the evidence actually supports, which is essential for a report intended to withstand external scrutiny.
Frequently asked questions
Is any correlation ever presented as a finding without further testing?
No. Correlations are treated as hypotheses requiring testing against alternative explanations and corroboration before being presented as findings in a final report.
What is confirmation bias and why does it matter here?
It is the tendency to favour information that supports an existing hypothesis while discounting information that does not. It is mitigated through structured review, including independent second review of key findings.
How is a base rate estimated in a blockchain context?
By examining how frequently a similar pattern occurs across a broader, unrelated sample of transactions or events, providing a benchmark against which an observed instance can be judged.
Does this cautious approach slow down investigations?
It adds a verification step, but it prevents costly errors caused by acting on unverified correlations, which is a greater risk to the investigation's integrity than the additional time required.
Correlation is a natural and often useful starting point in a digital asset investigation, but it is not evidence on its own. Testing alternative explanations, considering base rates, and requiring independent corroboration are what separate a rigorous finding from a superficially compelling coincidence.
