Simulation involves imitating the performance of a system using a model to generate sample experiments without observing the real system. Monte Carlo simulation specifically involves an element of chance and is useful when direct experimentation is impossible or too costly. It generates random numbers that can represent values of random variables to simulate observations. Common techniques include using uniform random numbers between 0 and 1 to represent distributions and the Box-Muller method for generating normal random variables.