Final answer:
The representativeness of a sample affects its accuracy when making inferences about a population. Accurate inferences depend on representative samples free from sampling bias, where random sampling helps achieve accuracy.
Step-by-step explanation:
The representativeness of a sample affects the accuracy of an inference to a population. When a sample is representative, it reflects the true characteristics of the population, thereby making the inference more accurate. In inferential statistics, we use sample data to make estimates about population parameters, recognizing that while the point estimate will not be the exact population value, it should be close to it.
Problems such as sampling bias can arise when a sample is not representative of the population, leading to sampling errors and unreliable results. Reliability in statistics refers to the consistency of a measurement, and accurate results depend on a lack of systematic errors. Ensuring a sample is representative through techniques like random sampling can provide an accurate estimate of public opinion or other characteristics of the population.