Final answer:
The risk of a different outcome between a sample-based decision and a population-based decision is called sampling error, which includes chance error and bias. To reduce such errors, larger and randomized samples are key, as is the critical evaluation of statistical studies.
Step-by-step explanation:
The risk that the decision made based on the sample will differ from the decision made based on the entire population is referred to as sampling error. This error reflects the natural variation that results from using a sample to represent a larger population. The concept of a representative sample is essential in this context, as it should be a subset of the population that reflects the population's overall characteristics as closely as possible.
There are two primary types of errors associated with sampling: chance error and bias. Chance error is related to the random variation inherent in sampling and is often mitigated by increasing the sample size. Bias, on the other hand, occurs when the sample selection process systematically excludes certain groups from the population, thereby affecting the sample's representativeness. A common example of bias is sampling bias, where the method of sample selection gives certain members of the population a lower chance of being selected.
To minimize these errors and improve the study's validity, researchers carefully select randomized and larger samples. Critical evaluation of statistical methods and data is necessary to ensure accurate conclusions about the population being studied.