Samples and Populations
Ever wonder how TV networks predict election winners with only a fraction of votes counted? That's sampling in action! A population is the entire group you want to learn about, while a sample is just a portion of that population used to make conclusions about the whole group.
Getting data from an entire population can be expensive, time-consuming, or sometimes impossible. That's why researchers use carefully selected samples instead. The key is making sure your sample truly represents the population.
An unbiased sample is selected randomly and is large enough to accurately represent the population. A biased sample, however, favors certain parts of the population over others, giving skewed results.
Quick Tip: Think of sampling like checking a few french fries to see if they're salty enough. If you only check fries from the top, you might miss that the ones at the bottom have no salt!
When identifying populations and samples, always ask: what's the complete group (population) and what's the smaller selected group (sample)? For example, all grizzly bears in a park (population) versus only those bears with GPS collars (sample).