Here’s something to keep in mind when you hear someone reach a conclusion about a large population.

An inductive generalization is when we draw a conclusion about a population based on what we observe in a sample.

For example, we're making an inductive generalization if we say that all swans are white because every swan we've ever seen is white.

While this conclusion may sound reasonable, it's flawed, so we must be careful when making decisions based on an inductive generalization.

We haven't seen every swan that's ever been, is, or will be alive. There could be a black, red, green, or blue swan out there somewhere. And all it takes is one non-white swan to prove our conclusion false.

A hasty generalization is an informal fallacy where we conclude something about a population based on insufficient evidence. We jump to a conclusion without considering all the relevant variables.

Because we see two white swans is not enough evidence to conclude that all swans in the world are white. But, if we observe nine white swans out of a population of ten swans, we are on somewhat firmer ground in concluding that the last swan is also white.

To keep a hasty generalization from leading us to a wrong decision, look at the sample size and how well the sample represents the population. A large sample size that mirrors the larger population will give us more confidence that our generalization is accurate.