Inferential Analysis

Inferential analysis: The process of generalizing from a sample to a population; the use of a sample statistic to estimate a population parameter. Also called hypothesis testing.

To account for sampling error, inferential statistics use sampling distributions to make probabilistic predictions about the sample statistic that is being analyzed. The basic strategy is a two-step process: First, a sample statistic is computed. Second, a probability distribution is used to find out whether this statistic has a low or high probability of occurrence. This process should sound very familiar—we already followed these steps when we worked with z scores and areas under the standard normal curve. Hypothesis testing is an expansion of this underlying idea. A sample statistic (such as a mean) can be used to find out whether this value is close to the center of the distribution (i.e., has a high probability of occurrence) or far out in the tail (low probability of occurrence). It is this probability assessment that guides researchers in making decisions and reaching conclusions.

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