How confirmation bias can skew incident learning and how to counter it in PagerDuty Incident Responder.

Outages don’t just test systems; they test teams. When alarms scream and dashboards glare, the instinct to find a straightforward cause can be strong. We want clarity, speed, and a sense that we’ve got a handle on what happened. But that hunger for an easy answer sometimes strolls in with a gatecrasher: confirmation bias. It’s that nagging tendency to search for, interpret, and remember information in a way that validates what we already think. And in the world of incident response, that bias can quietly flatten the path to true learning.

What confirmation bias looks like in incident reviews

Let me explain what this looks like in practice. Imagine you’re part of an incident response team using a PagerDuty-based workflow. The alert came from a specific service, and your gut tells you the root cause must be a misconfiguration in that service. So the team leans into that hypothesis, pulling logs and metrics that support it while downplaying or discarding data that points elsewhere. Sound familiar? It happens more often than you’d think.

Here are some recognizable patterns:

• Chasing a preordained culprit: If you’ve fixed a similar outage by adjusting a particular component, you might see that component as the villain in every new outage, even when other factors are at play.

• Data selection bias: When you cherry-pick logs or metrics that corroborate your hypothesis, you miss pieces of the puzzle that could tell a different story.

• Memory distortions after the fact: In the heat of the moment, memories can shift. What you remember may overemphasize the role of a familiar failure mode and understate surprises.

• Narrow post-incident reviews: Rather than stepping back to look at the whole system, teams focus on the fastest or simplest explanation, leaving systemic issues unexamined.

The blunted edge of biased analysis

Why is this a problem? Because learning from incidents is supposed to broaden your view, not shrink it. When confirmation bias takes the steering wheel, you risk:

• Missing systemic problems: A single faulty assumption can hide broader issues in architecture, processes, or culture.

• Repeating the same mistakes: If you’re convinced you know the root cause, you might implement a narrowly tailored fix that doesn’t prevent related failures.

• Eroding trust: Teams lose faith in post-incident reviews when answers feel rushed or one-sided. That trust matters just as much as the fix itself.

The flip side isn’t nothing, either. It’s tempting to think bias helps you move quickly. After all, speed is valuable in incidents. The reality, though, is speed without accuracy is a brittle win; you’re patching a wound that’s likely to reopen because you never treated its underlying cause.

A practical mental model to keep bias in check

Here’s a helpful approach: treat incident analysis like a guided experiment with clear hypotheses and counters. Start with the assumption that you might be wrong. In other words, ask yourself early, “What would prove my hypothesis wrong?” and plan to test that possibility.

A few concrete steps fit nicely into this model:

• State hypotheses explicitly at the outset. For example: “The outage was caused by a database contention under peak load.” Then add a contrarian question: “What data would contradict this claim?”

• Collect evidence from multiple sources. Logs, traces, metrics, runbooks, on-call notes, and even chat transcripts can all shine a different light. Don’t rely on one data stream to tell the whole story.

• Use structured analysis techniques. Root-cause analysis is great, but weave in a couple of checks:

• Five whys with verification: Keep digging, but pause to verify each why with evidence.

• Timeline reconstruction: Build a single, coherent chronology that includes anomalies, alerts, human actions, and system changes.

• Data-driven sanity checks: If you think a certain service failed, confirm cross-service signals and dependencies before locking in the cause.

• Invite diverse viewpoints. Different teams—SREs, developers, product managers, and sometimes on-call operators—bring lenses you may lack. A blameless review culture helps people speak up without fear.

• Document hypotheses and their refutations. A clear trail shows how you arrived at the final conclusions and helps future teams learn even when initial assumptions were wrong.

Blameless reviews: turning a fragile habit into a durable habit

Blameless postmortems aren’t softening the truth; they’re strengthening your learning. In a culture that prizes accountability without punishment, people feel safe to challenge ideas, question data, and surface weak spots. It’s not about who was at fault; it’s about what happened, why it happened, and what to do next to prevent it.

In practice, a blameless review might look like this:

• A documented timeline with timestamps and concrete observations.

• A set of testable hypotheses, each paired with evidence that supports or refutes it.

• Actionable improvements—changes in automation, monitoring, runbooks, or on-call playbooks—that are tracked to completion.

• A feedback loop that checks whether those changes actually reduced the risk of recurrence.

This approach reduces defensiveness and fosters a learning mindset. If a team feels the review is a chance to improve, bias loses its grip and the organization gains resilience.

The tools and rituals that reinforce objective analysis

Incident responders—whether you’re on a PagerDuty-powered stack or another platform—don’t operate in a vacuum. The right rituals and tools help curb bias before it sours the analysis.

• Hypothesis-driven reviews: Write down what you think happened, then seek data that confirms and contradicts it. Treat evidence as the referee, not the verdict.

• Premortems and pre-briefs: Consider potential failure modes before you deploy or change critical components. It’s a futurist exercise that makes your prevention plan stronger.

• Structured RCA templates: Move beyond “the root cause was X.” Include contributory factors, evidence, and a map of how different parts of the system interacted during the incident.

• Checklists and data sources: Use a consistent rubric to gather data—logs from the service, traces from distributed systems, metrics around latency and error rates, and the human side notes from the on-call crew.

• Diverse participation: Schedule reviews that invite engineers from related services, QA, or platform teams. Fresh eyes can spot something others missed.

• Temporal analysis: Align the incident narrative with the exact sequence of events. A precise timeline often reveals dependencies and timing relationships that bias misses.

• Data-driven dashboards: Lean on metrics and signal quality. A dashboard can surface anomalies you might overlook in a free-form discussion.

A few practical examples you can borrow

• If you suspect a configuration error, test for the same condition under different loads and traffic patterns. Compare behavior across blue/green deployments or canary shifts.

• If you think a race condition is to blame, replay a synthetic workload that mimics peak traffic. Look for timing windows where services miscommunicate.

• If a known issue seems to be the culprit, probe for evidence of new changes in the same component within the incident window. Sometimes a fresh patch interacts unpredictably with existing code.

Culture and leadership: the quiet engine

All the methods and tools won’t work if the culture doesn’t buy in. Leaders set the tone. They model humility, encourage dissent, and frame failures as learning opportunities rather than as personal indictments. It helps to remind teams that bias is a universal human trait, not a moral failing. The goal is to create an environment where questions are invited, data is king, and the truth—the full truth—has a say in the final story.

You’ll hear people talk about “psychological safety” a lot—and with good reason. It’s the bedrock of honest post-incident discussions. Without it, people opt for the easiest story to tell, which is rarely the most accurate one. In the PagerDuty incident responder world, this means leaders championing blameless reviews, setting expectations for evidence-based conclusions, and recognizing teams that push back on easy-but-wrong explanations.

Real-world takeaways: what teams can do next

• Start the PIR with a clear, testable hypothesis list. Keep it visible for everyone in the room.

• Build a habit of collecting evidence across data domains: telemetry, runbooks, on-call notes, and user impact reports. Corroboration beats confirmation.

• Design your review to surface contradictory data. If you can’t find any, that’s a signal to look again.

• Normalize dissent as a positive force. Encourage questions like, “What would we see if this hypothesis isn’t true?”

• Track improvements and measure their impact. If you implement a change, watch for changes in incident frequency, mean time to detect, and mean time to repair.

• Remember the human side. People who feel heard and valued are more likely to bring up alternative explanations and contribute to a richer understanding.

A practical mindset shift you can use today

Here’s a simple question you can carry into your next incident review: “What would prove me wrong?” If you can answer that with a concrete data plan, you’ve put bias in its place. You’re not denying your gut; you’re tempering it with evidence. It’s a small pivot, but it makes a big difference over time.

The bottom line

Confirmation bias isn’t a villain in disguise, but it is a bias that can derail learning from incidents if you let it run unchecked. In the world of incident response, where every minute counts and the stakes are real, a bias-aware approach helps teams uncover the true causes and build stronger defenses for the next outage. By combining explicit hypotheses, diverse perspectives, and structured, evidence-driven reviews, you create a durable framework for learning—one that makes you faster, more accurate, and more trustworthy as responders.

If you’re working with incident data and tooling—whether you’re analyzing dashboards, tracing requests across services, or coordinating responders—the aim is clear: move toward a fuller picture. Let the data lead where it can. Welcome the counterexamples. Build a culture where post-incident conversations deepen understanding, not confirm preconceptions. That’s how resilience grows—one thoughtful review at a time. And if you’re curious about how to translate these ideas into your day-to-day incidents, start with the basics: document hypotheses, gather diverse data, and invite those dissenting voices to the table. The rest tends to follow.