ABSTRACT
This chapter covers sampling statistics and tests of statistical significance. Sampling statistics guide decisions that allow inferring who will win an election and how Americans feel about the economy. It discusses tests of significance, which help to decide if a relationship that exists in data from a sample probably occurred by chance. Tests of significance simply eliminate chance as an explanation for the relationships; however, the statistical findings may stimulate more rigorous research to explain why a relationship exists and to eliminate alternative explanations. To apply and interpret sampling statistics correctly one need to understand the terms parameter, sampling error, standard error, confidence interval, and confidence level. Social scientists have recommended four alternatives to the traditional significance test. Sampling statistics allow estimating the value of a population characteristic, the parameter. The relationship may be unimportant or weak in the population. Sampling statistics are straightforward and less subject to misinterpretation.
