Simple Random

The conventional approach of sampling is commonly called Simple Random or Monte Carlo. In Simple Random sampling, a pseudo-random number generator is used for generating random numbers from 0 to 1.

Design points are generated by using the Inverse Transform method. Clustering may occur in the design point distribution because the sequence of samples is random.
Figure 1.

Usability Characteristics

  • The statistical measures (such as mean or standard deviation) of a random sample group requires large numbers of runs to converge the given probability distribution’s statistical measures.
  • A correlation structure can be specified to reflect the correlation existing between random variables. Applying a correlation structure can be costly for a large number of input variables.


In the Specifications step, Settings tab, change method settings.
Parameter Default Range Description
Number of Runs 100 > 0 Number of new designs to be evaluated.
Random Seed 1 Integer

0 to 10000

Controlling repeatability of runs depending on the way the sequence of random numbers is generated.
Random (non-repeatable).
Triggers a new sequence of pseudo-random numbers, repeatable if the same number is specified.
Apply User Correlations On Off or On Apply user specified correlations on the data.