Definition · forecasting
Forecast bias
Forecast bias is a systematic tendency to over- or under-forecast and how to detect and correct it. For forecast bias, the useful boundary is the driver, assumption, source data, owner, time period, scenario logic, and decision the model is meant to support.
Also known as forecast skew, planning bias
Why it matters
Understanding forecast bias matters because planning only improves decisions when assumptions, drivers, owners, and time periods are explicit enough to revisit when actuals arrive. When the term is tied to a source system, owner, and review cadence, it becomes easier to audit assumptions, catch changes early, and keep operators aligned.
In practice
Planning example
Teams use forecast bias when a forecast, budget, or scenario needs an assumption that can be revisited. The finance team should know the driver, source data, owner, and period before using it in a model.
Review example
Forecast bias should be reviewed whenever the source system, calculation logic, time period, or decision owner changes. That keeps the definition useful instead of letting it drift into a label.
In practice, teams should define forecast bias with a clear source, owner, time period, and decision before they use it in reporting, planning, or operating reviews.
Understanding forecast bias matters because planning only improves decisions when assumptions, drivers, owners, and time periods are explicit enough to revisit when actuals arrive. When the term is tied to a source system, owner, and review cadence, it becomes easier to audit assumptions, catch changes early, and keep operators aligned.
A strong workflow for forecast bias separates the definition from the action: first agree what the term means, then decide how it is measured, when it changes, and who is accountable for the next step.
FAQ
What is forecast bias?
Forecast bias is a systematic tendency to over- or under-forecast and how to detect and correct it. For forecast bias, the useful boundary is the driver, assumption, source data, owner, time period, scenario logic, and decision the model is meant to support.
How do you correct forecast bias?
To use forecast bias, start with the decision, then confirm the source data, timing, calculation logic, and owner. The analysis is strongest when a reviewer can trace the answer back to the records that produced it.