Assumptions

Not a statistician?

New to simulation? Start with the plain-English getting started guide first — it explains the key concepts in business terms.

An assumption is any cell in your model where the value isn't certain. Instead of typing a single number, you mark it as a range — and the simulation will try many values within that range to show you the spread of results.

Assumptions are the uncertain inputs in your spreadsheet simulation. You define which cells contain uncertain values and describe their uncertainty with a probability distribution. Each simulation iteration overwrites those cells with freshly sampled values before the workbook recalculates.

See Overview for how assumptions fit into the overall simulation workflow, and Running simulations for how iterations are executed.

Core concepts

Assumption — a single cell you've tagged as uncertain. Instead of a fixed value, the cell receives a random sample on each iteration drawn from the distribution you choose.

Distribution — a mathematical model of uncertainty. You pick one that matches what you know: use normal when values scatter symmetrically around a mean, triangular when you have a low/most-likely/high estimate, uniform when any value in a range is equally plausible, and so on.

Sampling — on each iteration the simulation engine draws one value per assumption and writes it to the cell before recalculating the workbook. After all iterations finish, the spread of output values reflects the combined uncertainty of all your assumptions.

note

Assumptions are always single-cell references. You cannot attach a distribution to a range or a named region — target one cell per assumption.

Adding an assumption

1. In the sidebar, click + Add under Assumptions. The Add Assumption panel opens on the right.
2. Fill in the fields:
   • Name — a label for this assumption (for example, Unit cost).
   • Sheet — select the sheet that contains the target cell.
   • Cell — enter the cell reference (for example, B4).
   • Distribution — choose a distribution from the dropdown.
   • Parameters — enter the parameter values for the chosen distribution.
3. Click Add. The assumption appears in the sidebar list and the target cell is highlighted in blue in the spreadsheet view.

tip

Match the distribution to what you actually know. If you have historical data, normal or lognormal usually fit well. If you're working from expert judgment alone, triangular is a good choice — it requires only a pessimistic, most-likely, and optimistic estimate.

Editing and removing assumptions

1. To edit an assumption, click the edit icon next to it in the sidebar. The panel reopens with the current values pre-filled.
2. Change any fields, then click Save. The cell highlight and sidebar entry update immediately.
3. To remove an assumption, click the remove icon. The assumption is deleted and the blue highlight is removed from the spreadsheet.

Reference

Fields

Field	Description
Name	User-defined label displayed in the sidebar
Sheet	Sheet name from the IronCalc sheet list
Cell	Single cell reference (for example, B4)
Distribution	Probability distribution used for sampling
Parameters	Distribution-specific parameter values

Supported distributions

Distribution	Parameters	Description
constant	value	Always returns the same value — effectively a fixed input
normal	mean, std	Symmetric bell curve
lognormal	mean, sigma	Right-skewed, always positive
uniform	low, high	Any value equally likely in a range
triangular	low, mode, high	Three-point estimate
exponential	rate	Memoryless, right-skewed
gamma	shape, scale	Flexible positive-value distribution
weibull	scale, shape	Wear-out or reliability modeling
poisson	rate	Count-based, integer values