Best Practices

Learn proven techniques for creating accurate, reliable analyses that drive better decisions.

Foundation Principles

Start with Clear Objectives

Before building any model, clearly define:

• What decision(s) are you trying to make?
• When do you need to make them?
• What specific questions need answers?
• What level of accuracy is needed?
• Who will use the results and how?
• What is the desired level of confidence in the results?
• What might I learn along the way?

Embrace "Good Enough" Modeling

Perfect models don't exist. Focus on building models that are:

• Useful: Address the key decision
• Understandable: Stakeholders can follow the logic
• Actionable: Lead to clear next steps
• Timely: Available when the decision needs to be made

Model Building Best Practices

Keep It Simple

Start with the simplest model that addresses your question, then add complexity only if needed:

• Begin with 3-5 key Areas of Uncertainty or Decision
• Test simple versions before building complex ones

Managing Scenario Count

Once you know what decision and uncertainty areas are (see Inputs), total scenario count is the product of combinations across those areas. Bear Decisions evaluates every combination you define:

or, put another way:

Example Scenario Calculation:

• 2 Decision Areas with 2 and 3 options = 6 decision combinations
• 3 Uncertainty Areas with 3 cases each = 27 uncertainty combinations
• Total scenarios = 6 × 27 = 162 scenarios

Validate Your Inputs

Your outputs are only as good as your inputs:

• Use multiple sources: Don't rely on single data points
• Check historical data: How accurate were past estimates?
• Get expert input: Validate assumptions with people who know
• Calibrate your uncertainties: Go through exercises to identify biases and ensure you are covering an appropriate range of cases for each uncertainty area. Discuss with peers to ensure a shared understanding of what good and bad outcomes might look like.
• Test extreme values: Make sure your ranges are realistic

Document Everything

Future you (and others) will thank you:

• Record data sources and assumptions
• Explain why you chose specific alternative values for each Area of Uncertainty or Decision as well as the probability assigned to each uncertaintycase
• Note any limitations or concerns
• Keep track of model versions and changes

Avoid the Overconfidence Trap

People tend to be overconfident in their estimates. Be aware of this and try to challenge your assumptions:

• Make ranges wider than your first instinct
• Ask "What could go wrong?" and "What could go better than expected?"
• Challenge assumptions with devil's advocate thinking

Sanity Check

Always test your model:

• Extreme value testing: Set variables to min/max and see if results make sense
• Order of magnitude checks: Are results in the right ballpark?
• Trend analysis: Do results change in expected directions when inputs change?
• Historical comparison: How do results compare to past similar situations?

Sensitivity Testing

Understand what drives your results:

• Identify the most influential variables
• Test different distribution assumptions
• Vary correlation assumptions
• Check robustness to outliers

Communication

Decks, memos, stakeholder-specific framing, and how to lead with insight—without duplicating this page—are covered in Communication.

Organizational Best Practices

Build Modeling Capability

Create sustainable modeling practices:

• Train multiple people, not just one expert
• Develop standard templates and approaches
• Create review processes for important analyses
• Build a library of past analyses for reference

Establish Governance

For critical decisions, establish clear processes:

• Define who can approve model assumptions
• Require peer review for high-stakes analyses
• Document model validation procedures
• Archive models and results for future reference

Learn from Results

Track how your models perform:

• Compare predictions to actual outcomes when possible with lookback analysis efforts
• Identify patterns in modeling errors
• Update future models based on lessons learned
• Share learnings across the organization

Ethical Considerations

Avoid Bias

Be aware of potential biases:

• Confirmation bias: Looking for results that confirm preconceptions
• Anchoring bias: Being overly influenced by initial estimates
• Availability bias: Overweighting recent or memorable events
• Optimism bias: Being unrealistically positive about outcomes

Consider Stakeholder Impact

Think about who is affected by your analysis:

• Are there groups who might be harmed by the decision?
• Have you considered unintended consequences?
• Are you transparent about limitations and assumptions?
• Do stakeholders understand the uncertainty in your results?

Final Recommendations

Remember that the goal of analysis is better decisions, not perfect models. Focus on:

• Clarity: Make your analysis easy to understand and act on
• Honesty: Be transparent about uncertainty and limitations
• Relevance: Address the actual decision being made
• Timeliness: Deliver insights when they're needed

The best analysis is the one that leads to better decisions. Keep that goal in mind, and you'll create valuable, actionable insights.

Next Steps

Continue to Settings & Configuration →