# Uncertainty Setup dialog/Uncertainty sample/4.6

## Uncertainty setup dialog

This archival page covers the Uncertainty Setup dialog's Uncertainty Sample pane as it exists in Analytica 4.6.

## Uncertainty sample pane

The default dialog shows only a field for sample size. To view and change the sampling method, random number method, or random seed, press the More Options button.

Sample size: This number specifies how many runs or iterations Analytica performs to estimate probability distributions. Larger sample sizes take more time and memory to compute, and produce smoother distributions and more precise statistics. See Selecting the Sample Size for guidelines on selecting a sample size. The sample size must be between 2 and 32,000. You can access this number in expressions in your models as the system variable SampleSize.

### Sampling method

The sampling method is used to determine how to generate a random sample of the specified sample size, m, for each uncertain quantity. Analytica offers three options: Monte Carlo, Median Latin hypercube (the default), and Random Latin hypercube sampling methods. See Monte Carlo and probabilistic simulation for details.

### Random seed

This value must be a number between 0 and 100,000,000 (108). The series of random numbers starts from this seed value when:

• A model is opened.
• The value in this field is changed.
• You check the Reset once box, and close the Uncertainty Setup dialog by clicking Accept or Set Default.

### Reset once

Check the Reset once box to produce the exact same series of random numbers

### Random number method

The random number method is used to determine how random numbers are generated for the probability distributions. It is extremely rare that any Analytica user will need to worry about the differences between these methods, and use anything other than the default method. For those that do, it offers three different options:

• Minimal Standard (the default method): The Minimal Standard random number generator is an implementation of Park and Miller’s Minimal Standard (based on a multiplicative congruential method) with a Bays-Durham shuffle. It gives satisfactory results for less than 100,000,000 samples.
• L’Ecuyer: The L’Ecuyer random number generator is an implementation of L’Ecuyer’s algorithm, based on a multiplicative congruential method, which gives a series of random numbers with a much longer period (sequence of numbers that repeat). Thus, it provides good random numbers even with more than 100,000,000 samples. It is slightly slower than the Minimal Standard generator.
• Knuth: Knuth’s algorithm is based on a subtractive method rather than a multiplicative congruential method. It is slightly faster than the Minimal Standard generator.