In Sections 3 and 17, you learned the importance of using random samples whenever possible. By using random sampling, researchers draw unbiased samples. However, an unbiased random sample still contains random sampling errors. In other words, by the luck of the random draw, a random sample may differ from a population in important aspects. Fortunately, sampling errors can be evaluated with inferential statistics, which is the topic of the remaining sections in this part of the book. It is important to note, however, that inferential statistics cannot be used to evaluate the role of bias, which is why it is important to eliminate bias in the first place by using random sampling.