gen-briefly-explain-sampling-bias-and-one-common-misconception-a-2cf0

Briefly explain sampling bias and one common misconception about it.

## Sampling Bias **Sampling bias** occurs when some members of a population are systematically more likely to be selected for a sample than others, resulting in a sample that doesn't accurately represent the intended population. **Example:** Conducting a phone survey only on landlines will oversample older populations and miss younger people who primarily use cell phones. ## Common Misconception **"Larger samples automatically eliminate bias"** Many people believe that simply increasing sample size fixes sampling problems. However, a large biased sample is just a *precisely wrong* estimate. For instance, surveying 10,000 people at a gym about exercise habits still won't represent the general population's fitness levels—it will just give you a very precise estimate of gym-goers' habits. **The key:** How you sample matters more than sample size. A smaller, properly randomized sample beats a larger biased one.