The Research Corroboration Problem: How to Know Which Insights Are Actually Reliable

Research has a dirty secret: most of what you read is either wrong or incomplete. Studies fail to replicate. Experts contradict each other. Today's best practice is tomorrow's anti-pattern. How do you know which insights to trust?

The traditional answer is "consider the source"—trust authoritative experts and established publications. But expertise is narrower than it appears, publication incentives are misaligned, and even genuine experts often disagree.

A better answer: corroboration. Instead of asking "who said this?" ask "how many independent sources support this?" An insight confirmed by multiple unconnected sources is more reliable than one from a single source, regardless of that source's authority.

This article presents a framework for tracking corroboration systematically—turning scattered research into a reliability-scored knowledge base.

We're taught to evaluate sources: peer-reviewed journals are better than blogs; experts are better than amateurs. This heuristic is useful but fundamentally limited.

Over 50% of published psychology research fails to replicate. Medical studies have similar problems. Peer review catches obvious errors but not subtle methodological issues. "Published in a journal" is not a reliability guarantee.

In any complex domain, experts disagree. Software architecture? Ask ten senior engineers, get twelve opinions. Nutrition science? Depending on which expert you ask, carbs are either essential or poisonous. Expert opinion reflects individual experience and theoretical commitments as much as objective truth.

Content creators—even honest ones—have incentives that don't always align with accuracy:

• Researchers need novel findings to publish (boring replications don't advance careers)
• Writers need engaging takes (nuanced uncertainty is less clickable)
• Consultants need differentiated opinions (agreeing with everyone isn't valuable)

This doesn't make sources dishonest. It makes them systematically biased toward novelty and confidence over accuracy and uncertainty.

Instead of trusting any single source, triangulate across multiple sources. When independent sources reach similar conclusions through different paths, the probability that they're all wrong decreases dramatically.

Corroboration tracking assigns reliability scores based on independent confirmation. Here's how it works:

A single source with no independent confirmation. This is the default state for new information. Treat with skepticism. Useful for generating hypotheses, not for confident action.

Two independent sources reaching similar conclusions. "Independent" means they didn't copy from each other and ideally approached the question differently. This is the minimum bar for provisional trust.

Three or more independent sources in agreement. At this level, you can act with reasonable confidence while remaining open to revision.

Broad consensus across multiple sources, including practitioners with real-world experience. At this level, the insight has been tested by application, not just assertion.

Sources actively disagree. This is valuable information—it means the question is genuinely unsettled. Don't pick a side arbitrarily; track both perspectives until additional evidence resolves the disagreement.

Two sources aren't better than one if the second just copied from the first. True corroboration requires independence.

Sources are independent when they:

• Don't cite each other (no direct copying)
• Come from different contexts (different industries, domains, or timeframes)
• Reach similar conclusions through different reasoning
• Have different incentives (one isn't paying the other)

Example of dependent sources: Three blog posts that all cite the same research paper. They're not independent—they're amplifying a single source.

Example of independent sources: A research paper, a practitioner's blog post describing similar findings from experience, and a different researcher's work reaching comparable conclusions through different methodology.

Not all corroboration is equal. Weight confirmations by:

• Source expertise: Domain expert > adjacent domain > generalist
• Evidence type: Empirical observation > reasoned argument > speculation
• Skin in the game: Practitioner with real stakes > commentator without stakes

Three expert practitioners confirming something is stronger than ten casual observers agreeing.

How do you actually track this? Here are practical approaches:

When capturing insights, include a corroboration field:

• Insight: "..."
• Source: "[original source]"
• Corroboration: [Level 0-3 or Contradicted]
• Supporting sources: [list any additional sources]

When you encounter the same insight from a new source, update the corroboration level and add the source to the list.

Tools like Refinari track corroboration automatically. When you add content that contains insights similar to existing ones, the system:

1. Detects the similarity using semantic matching
2. Asks whether to create a new insight or corroborate the existing one
3. If corroborating, increments the counter and logs the new source
4. Updates the reliability display accordingly

Over time, your most-corroborated insights float to the top. When you query for knowledge, you can filter by reliability level.

When sources contradict each other:

1. Record both positions with their respective sources
2. Note the key disagreements
3. Track whether subsequent sources support one side
4. Don't resolve artificially—live with the uncertainty until evidence tips the balance

Here's how corroboration tracking plays out in practice:

You're researching whether microservices are appropriate for your project. Sources you encounter:

1. Conference talk: "Microservices improved our team's velocity" (single anecdote)
2. Blog post: "We switched to microservices and now deployment is faster" (another anecdote)
3. Book chapter: "Microservices trade development simplicity for operational complexity" (principle)
4. Practitioner interview: "We regret going to microservices too early—now we're consolidating" (counter-anecdote)
5. Engineering report: "Most successful microservices adoptions happen after hitting specific scaling limits" (qualified guidance)

With corroboration tracking, the picture becomes clear:

• "Microservices can improve deployment speed" - Level 1 (two supporting anecdotes)
• "Microservices add operational complexity" - Level 2 (multiple independent confirmations)
• "Premature microservices adoption is risky" - Level 2 (multiple sources, including practitioner experience)
• "Wait until specific scaling limits before adopting" - Level 1 (one strong source)

The synthesis: Microservices can help but add complexity. Wait until you have clear scaling problems. This conclusion is more reliable than any individual source because it's corroborated across multiple perspectives.

Corroboration tracking isn't perfect:

If all your sources come from the same community, they might be corroborating each other's assumptions rather than independent truth. Actively seek sources from outside your usual domains.

Once something reaches "established," there's a risk of ignoring contradicting evidence. Periodically review high-corroboration beliefs with a skeptical eye.

New ideas by definition lack corroboration. Don't dismiss Level 0 insights—just weight them appropriately. Sometimes the unverified insight is the breakthrough.

Corroboration works best for empirical claims. For subjective matters (aesthetics, values, preferences), agreement doesn't establish correctness. Use corroboration for claims about reality, not for matters of taste.

In a world of abundant, often contradictory information, single-source authority isn't enough. Corroboration tracking provides a systematic way to assess reliability—not by trusting any individual source but by noting when multiple independent sources converge.

The framework is simple: Level 0 (unverified) through Level 3 (established), with special attention to contradictions and the independence of confirming sources. Implement it manually or use tools that track corroboration automatically.

The result: a knowledge base where you can see at a glance which insights are speculative versus well-supported. When it's time to act, you'll know which knowledge to trust—and which to test further before relying on.