From Theory to Practice: Making Minimum Viable Rigor Work for Real Teams

Read Carl's original piece here: https://carljpearson.com/minimum-viable-rigor/

There are two tribes in research. Tribe One worships rigor like it's the only thing standing between them and professional shame. Every study needs to be a masterpiece with perfect sampling, bulletproof constructs, and analysis so pristine you could frame it. Tribe Two treats rigor like seasoning—sprinkle a little on top and call it done. Deadlines loom, stakeholders want numbers they can quote in meetings, and "good enough" isn't an insult, it's Tuesday.

Carl J. Pearson's piece on Minimum Viable Rigor cuts through this false choice. You don't need to build a cathedral for every question. But you also can't slap together a research shack and hope it doesn't collapse when someone actually tries to use your findings. What you need is enough rigor to make solid decisions without turning every insight into dust.

When I read Carl's article, it hit me that his MVR concept was brilliant but incomplete. He gave us the philosophical framework, but most teams would read it, nod along, then immediately get stuck on the "okay, but how do we actually do this?" part. That's the gap I kept thinking about—how do you take his elegant idea and turn it into something teams can actually run with, day after day, without endless debates about what constitutes "enough" rigor?

This post does three things. First, it builds on Carl's foundation and explains why his framing matters so much. Second, it translates the concept for people who think in terms of practical trade-offs. Third, it gives you the missing operational pieces so your team can actually use MVR without turning every project kickoff into a philosophy seminar.

Why Carl's article matters

Carl's core insight is both simple and game-changing: rigor isn't about academic purity, it's about decision quality. He uses this great image of truth as a fragile artifact. Handle it carefully and you preserve what matters. Go at it with a sledgehammer and you end up with impressive-looking debris that can't actually hold water when you need it to.

His rule of thumb is elegantly practical: Minimum Viable Rigor = Rigor of Insight - Decision Risk. In plain English: when the stakes go up, your research standards go up. When stakes are low, you can move faster. This reframes the whole conversation from a moral argument about research purity into a practical discussion about risk tolerance.

The problem is that most teams read this and think "great, but how do I actually apply this tomorrow?" That's what the rest of this post tackles.

A note before we dive in

What follows is a working model, not a universal law. The scoring, bundles, and thresholds in this post are examples of how you could turn Minimum Viable Rigor into something operational. Every team needs to define its own cutoffs based on context, industry, and appetite for risk.

If you build software for hospitals or banks, your definition of “medium risk” will be very different from mine. If you are tweaking button colors on a casual game, some of these steps may be more rigorous than you will ever need. The goal is not to copy numbers from here into your process but to start talking about risk and rigor in measurable terms.

Before you dive in, make sure you have read Carl’s original article. This post only makes sense if you understand the foundation he laid. His piece is the frame. What follows here is just one way to build scaffolding on top of it.

Part One: Risk categories that actually help you make decisions

Stop arguing about whether something is "low risk" or "high risk." Score it instead. Use these six factors, rate each from 0-5, higher means riskier:

Impact on users
0 = minor inconvenience for a few people
5 = potential harm or major disruption for many

Business exposure
0 = negligible revenue or cost impact
5 = significant revenue shift or major cost exposure

Reversibility
0 = can roll back same day
5 = hard to undo or irreversible

Scope of exposure
0 = tiny test with small audience
5 = wide rollout that lots of people will see

Legal or compliance sensitivity
0 = none
5 = regulated space or likely to trigger legal review

Brand or trust risk
0 = nobody will notice or care
5 = potential for public backlash or lasting trust damage

Add up your six scores to get your risk score (R).

• 0-8: Low risk
• 9-16: Medium risk
• 17-30: High risk

Two safety checks: If any single factor scores a 5, treat it as at least medium risk. If two or more factors hit 5, it's automatically high risk.

This gives you something concrete to point to when someone questions your approach.

Part Two: Method bundles that match your risk level

Pick the bundle that matches your risk tier, then stick to the quality standards. These are templates, not commandments—adapt them for your context, but make sure your versions still have real guardrails.

Low Risk Bundle

Goal: Quick directional evidence to choose between small options

Core methods:

• Task-based usability with 3-5 actual users
• One-page survey with 10-30 targeted responses
• Quick telemetry check of current performance
• Brief scan of existing research and competitor patterns

Guardrails:

• Recruit from your actual audience (not just whoever's handy)
• Use a script with specific tasks and clear success criteria
• Capture evidence with clips or detailed notes
• Get at least two different types of evidence

Quality bar:

• Every recommendation backed by at least two converging signals
• Clearly label assumptions so people don't mistake hunches for facts

Medium Risk Bundle

Goal: Confident recommendation with explicit uncertainty

Core methods:

• Structured interviews with 8-12 people across key segments
• Task-based usability with timing and success metrics
• A/B test with one primary metric and preset success threshold
• Survey with 50-200 responses and proper sample composition

Guardrails:

• Define your primary metric and failure threshold before you start
• Document your plan in advance—questions, methods, sample size, decision criteria
• Get someone else to review both your plan and your findings
• Run through a bias checklist covering leading questions, order effects, cherry-picked analysis

Quality bar:

• Report results by segment or explicitly state you found no differences
• Show uncertainty with confidence intervals or at least error bars
• Store raw materials somewhere findable for later reference

High Risk Bundle

Goal: Evidence that can survive executive scrutiny and legal review

Core methods:

• Mixed methods with multiple phases
• Properly powered experiment with safety guardrails
• Longitudinal tracking to see behavior over time
• Expert review covering ethics and compliance

Guardrails:

• Write explicit harm analysis with prevention measures
• Pre-commit to analysis approach and success criteria
• Get independent check on your data and conclusions
• Invite critics to challenge assumptions and poke holes

Quality bar:

• Evidence from at least three independent sources that agree
• Clear decision criteria and rollback triggers for launch
• Documentation you'd be comfortable making public

Part Three: A decision framework with actual numbers

People fight about rigor because there's no shared metric. Put numbers on it, then let the numbers settle arguments.

You need two scores:

A. Risk score (R)
Use your score from Part One, then normalize it by multiplying by 35/30 to match the method scale. We normalize because risk scores range 0-30 while method scores range 0-35, and we need them on the same scale for the margin calculation to be meaningful.

B. Method rigor score (M)
Rate your research plan on these seven dimensions, 0-5 each:

1. Construct validity - Do your measures actually capture what you claim?
2. Reliability - Would you get similar results if you ran it again?
3. Representativeness - Right people, right contexts?
4. Bias control - Safeguards against recruitment, wording, analysis, researcher bias?
5. Measurement quality - Clear definitions for tasks, metrics, data collection?
6. Transparency - Plan documented, raw materials stored?
7. Triangulation - Multiple independent sources that converge?

Add them up to get M (ranges 0-35).

Calculate your margin: M minus normalized R

• Margin 6+: Above MVR line, proceed
• Margin 1-5: Borderline, add one guardrail or extra method
• Margin 0 or below: Below MVR line, redesign plan or reduce risk

Why these thresholds? The margin of 6+ assumes you need a meaningful buffer above the minimum requirements—research rarely goes exactly as planned, and you want confidence even when things get messy. A margin of 1-5 signals you're close but should add safeguards. Zero or negative margins mean you're systematically underprepared for the decision stakes.

These numbers are calibrated starting points, not gospel. Over time, you may find your organization needs higher or lower margins based on your risk tolerance and the cost of being wrong. The value isn't in the precise numbers—it's in making the risk-rigor trade-off explicit and discussable.

The intake process that makes everything work

The key to making MVR practical is having a consistent way to evaluate every research request. No more “quick test” favors, no more mystery risk levels. Every project starts here.

The MVR Intake Form: what to capture

Seven things. No more, no less:

1. Decision in one sentence – “Should we redesign the checkout flow?” is clear. “We need to understand our users” is not.
2. Audience – Who exactly are we studying? Be recruitable, not vague.
3. Risk scoring across six factors – Users, business, reversibility, scope, compliance, brand. Score 0–5 each, no skipping.
4. Timeline to decision – When do we actually need the answer, not when would we like it.
5. Primary success metric and failure threshold – Numbers, not vibes. Be specific.
6. Non-negotiable constraints – Budget, tech limits, regulation, deadlines.
7. Post-launch change triggers – What would make us roll back or reconsider after release?

How to use it

1. Add up the six risk factors → R (0–30).
2. Match your tier → 0–8 low, 9–16 medium, 17–30 high.
3. Choose methods from the right bundle, then score them on the seven rigor dimensions → M (0–35).
4. Normalize R (R × 35/30), then calculate Margin = M – normalized R.
   • Margin ≥ 6: Proceed
   • Margin 1–5: Borderline, add safeguards
   • Margin ≤ 0: Redesign or reduce risk
5. Save the form with your project files. It’s your audit trail and blueprint.

Making the form stick in practice

An intake form only works if it becomes part of your team’s operating system, not something you dust off when you remember.

• Make it mandatory. Every research request goes through it, even the “quick five-person usability test.” Those quick asks are often the riskiest in disguise.
• Run it live, not as homework. Don’t email it—book 15 minutes and fill it in together. When you ask, “What would make you change course after launch?” most stakeholders realize they never thought about rollback triggers.
• Let it drive the project. The risk score picks your bundle. The timeline defines what’s feasible. The metrics become your yardstick. The constraints shape the design. The intake isn’t intake—it’s the blueprint.
• Close the loop. After launch, revisit the form. Did the risk scoring hold up? Were the constraints real? Did the decision criteria actually get used? That’s how you calibrate and build institutional memory instead of just shipping and forgetting.

Use it as your shield. When someone pushes for a flimsy study on a high-risk call, let the numbers speak:

“This decision scores 18, which makes it high risk. Your proposed methods score about 12 on rigor. That leaves a negative margin. Either we increase rigor or reduce the risk—your choice.”

Three examples of the system working

Case A: Copy tweak on payment screen

Risk scoring: Impact 1, Business 2, Reversibility 0, Scope 1, Legal 0, Brand 1. Total = 5 (low risk)

Methods: Three usability sessions with target users, intercept survey with 20 responses, telemetry review of last week's drop-offs

Quality checks: Scripted tasks, predefined success criteria, two converging sources

Rigor score: Construct 3, Reliability 2, Representativeness 3, Bias 3, Measurement 3, Transparency 3, Triangulation 3. M = 20

Margin: Normalized R ≈ 6, so margin = 14. Proceed.

Outcome: Copy updated, drop-off improves 3%, team celebrates with coffee instead of cake (cake would raise the risk score).

Case B: Change default tip settings

Risk scoring: Impact 3, Business 3, Reversibility 2, Scope 3, Legal 1, Brand 2. Total = 14 (medium risk)

Methods: Structured interviews with 12 users across income levels, A/B test with preset threshold on revenue and churn, survey with 100 responses about perceived fairness

Quality checks: Pre-registered plan, independent peer review, bias checklist

Rigor score: M = 26, normalized R ≈ 16, margin = 10. Proceed.

Outcome: Effect holds with some segment differences, adjust rollout for two segments, stakeholders ask for more of whatever you just did. Nobody writes angry emails about tipping fairness.

Case C: Launch new credit product

Risk scoring: Impact 5, Business 5, Reversibility 4, Scope 5, Legal 5, Brand 4. Total = 28 (high risk)

Methods: Mixed methods program, powered experiment with guard metrics, longitudinal behavior tracking, external compliance review

Quality checks: Explicit harm analysis, pre-committed analysis plan, red team challenge, independent reproducibility check

Rigor score: M = 35, normalized R ≈ 33, margin = 2. Borderline.

Action: Add canary rollout to lower actual risk, margin clears, proceed in stages.

Outcome: Launch proceeds without regulatory investigation or user revolt. Legal team stops having nightmares.

Why this matters

People believe in Minimum Viable Rigor in theory. The gap has always been execution. This framework closes that gap with clear risk math anyone can learn quickly, method bundles with quality standards that protect truth under pressure, a margin rule that stops opinion fights and moves teams forward, and tracking that builds institutional memory instead of slide deck graveyards.

Carl gave us a frame we could rally around. This post adds the scaffolding to actually use it. Take it as-is, adapt it for your context, or let me know if you want me to turn the scoring into a quick worksheet or Notion template.

Read Carl's article first, then run this system on your next decision. Build something sturdy enough to live in without going broke on marble floors. That's Minimum Viable Rigor in practice, and it's how you ship with confidence more often than luck would allow.

I write one to three longform UX essays a week—half toolkit, half field notes, all designed to help researchers stop apologizing and start steering product decisions.

👉 Subscribe if you’d rather ship with confidence than drown in “directional” insights and unread slide decks.