Final answer:
Mistakes in evaluating sample items from a population exemplify sampling and non-sampling errors, including chance errors, selection bias, and factors unrelated to the sampling process, like data entry mistakes.
Step-by-step explanation:
Mistakes made in evaluating sample items that were appropriately selected from the population are an example of sampling errors and non-sampling errors. Sampling errors occur due to the natural variation in the sampling process, such as having a sample size that is too small, which leads to chance errors or selection bias if the sample is not properly randomized. On the other hand, non-sampling errors are caused by factors unrelated to the sampling process, like data entry errors, inaccurate information from respondents, or poor study design.
Sampling bias is a specific type of error that arises when the method of selecting a sample causes it to be unrepresentative of the population. An example of this would be conducting a survey only during certain hours which excludes a portion of the population, leading to incorrect conclusions. Response bias and nonresponse bias are other forms of biases that can affect the reliability of survey results. To provide reliable and valid conclusions, it is crucial to critically evaluate statistical studies for the presence of these errors and biases.