Just to give a practical example. I've been working on a product and I need sales data but I can't get sales data until I have the product. So what do I do?
I made a basic market simulation. In my simulation there is a demand curve which dictates how many buyers exist at or below a given price. From the demand curve I build a population of people each of which has a maximum they are willing to pay. Then I pick people out of this population at random and I pick time intervals between sales from a poisson distribution. In this way I can test many many different scenarios in terms of equilibrium price, sales volume and market size. In about an hour I can have well over a million simulated sales across an exhaustive range of product types.
I don't know for a fact that my product works outside the simulation, but I'm confident enough in the assumptions I made that it is worth trying. For simulated data, the question is always "will the expected loss from a bad assumption cost more than the cost of acquiring a real data set".
I made a basic market simulation. In my simulation there is a demand curve which dictates how many buyers exist at or below a given price. From the demand curve I build a population of people each of which has a maximum they are willing to pay. Then I pick people out of this population at random and I pick time intervals between sales from a poisson distribution. In this way I can test many many different scenarios in terms of equilibrium price, sales volume and market size. In about an hour I can have well over a million simulated sales across an exhaustive range of product types.
I don't know for a fact that my product works outside the simulation, but I'm confident enough in the assumptions I made that it is worth trying. For simulated data, the question is always "will the expected loss from a bad assumption cost more than the cost of acquiring a real data set".