Mavryck Blogs
Reference Class Forecasting
Reference Class Forecasting: How to Escape the Overconfidence Trap
Why Most Forecasts Fail
Project teams often rely on gut instinct or one-size-fits-all assumptions—leading to unrealistic timelines and budgets.
- Anchoring bias skews estimations
- Lessons from past projects are not applied
- Pressure to be optimistic undermines planning
Using Reference Class Forecasting with Mavryck
Mavryck’s AI identifies past project clusters and calibrates forecasts using statistical context, not assumptions.
- Detects optimal bias for each project type
- Benchmarks scope, duration, and cost trends
- Improves predictability across portfolios
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