Question

In our population models, we are modeling discrete populations (you can’t have 3.45 people) with continuous functions. Explain why this becomes less significant if we are modeling large populations as opposed to small populations.

Answer 1

The population models often assume the populations to be continuous when in fact they are discrete.

This becomes a problem where the populations are small as the estimates of the model would be fractional and estimating the results to the population would become highly error-prone or even many times too unreal.

However, as the population sizes increase, the continuous assumption does not impact much. This is because, for very large populations, the range of numbers covered is huge. Hence, fractional errors are too insignificant to affect the results of the model.

For instance, for a population of size 10, an estimate that would give a value of 5.57 would appear gibberish.

However, for a population of 1 million, an estimate of 0.57 million gives an integer value even when the estimate is rational, thereby resulting in the errors becoming insignificant.