![]() ![]() If the researcher drew another random sample, the next sample mean might be closer to 260. ![]() However, it’s conceivable that, due to sampling error, the mean of the population might be only 260. Unfortunately, the value of the population parameter is not only unknown but usually unknowable. The population parameter is μ, or mu, which is the average of the entire population. For our example, the sample statistic is the sample mean, which is 330.6. The sampling error is the gap between the sample statistic and the population parameter. However, the tradeoff for working with a manageable sample is that we need to account for sample error. There are huge benefits when working with samples because it is usually impossible to collect data from an entire population. ![]() Regrettably, the situation isn’t as clear as you might think because we’re analyzing a sample instead of the full population.
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