Sensitivity Analysis

Forecasts depend on many assumptions, but some matter much more than others. Sensitivity analysis identifies which assumptions have the biggest effect on the result.

For example, an analyst valuing a project might test what happens to its net present value if sales volume is 10% lower than expected, then if the selling price is 10% lower, then if costs are 10% higher. If a 10% fall in price wipes out all the project's value while a 10% rise in costs only reduces it slightly, the analyst knows that price is the key risk.

Results are often shown in a table, with different values of one or two assumptions along each side and the resulting outcome in each cell.

Sensitivity analysis helps decision-makers focus their attention. It shows which assumptions need the most careful research and monitoring. Its limitation is that, in reality, assumptions often change together, which is why it is commonly combined with scenario analysis.