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Reply: Monte Carlo analysis VS Sensitivity analysis

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Topic History of : Monte Carlo analysis VS Sensitivity analysis

Max. showing the last 6 posts - (Last post first)
1 year 8 months ago #23634

Gabriella Dellino, PMP

Gabriella Dellino, PMP's Avatar

Hi Mahdi,

In Monte Carlo simulation, you have some factors (variables) that can take values following a given probability distribution. When you run a MC simulation, you sample different values from the distribution and see (through computer simulations) how the system behaves, observing/measuring the resulting output produced. Since you are dealing with random variables, you need to run the simulations many times, and from the results you get you are able to build confidence intervals associated to the output.

Sensitivity analysis helps you establish how sensitive the system is to a given input (or combination of inputs), determining the correlation between the input and the output. Here you measure how a variation in the input(s) impacts on the resulting output.

Hope this helps,

Gabriella
1 year 8 months ago #23617

Mahdi Seify

Mahdi Seify's Avatar

Hi
Could you please let me know what is different? both definitions look similar
Monte Carlo: the combined effects of individual project risks or other sources of uncertainty to output the probability of certain events occurring
and the level of confidence that a specific event will occur.

Sensitivity analysis: helps determine, which individual project risks or other sources of certainty have the most potential impact on project outcomes.

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