Like every subject, statistics has its own language. The language is what helps you know what a problem is asking for, what results are needed, and how to describe and evaluate the results in a statistically correct manner. Here's an overview of the types of statistical terminology:

Four big terms in statistics are population, sample, parameter, and statistic:

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*population*is the entire group of individuals you want to study, and a*sample*is a subset of that group.A

*parameter*is a quantitative characteristic of the population that you're interested in estimating or testing (such as a population mean or proportion).A

*statistic*is a quantitative characteristic of a sample that often helps estimate or test the population parameter (such as a sample mean or proportion).

*Descriptive statistics*are single results you get when you analyze a set of data — for example, the sample mean, median, standard deviation, correlation, regression line, margin of error, and test statistic.*Statistical inference*refers to using your data (and its descriptive statistics) to make conclusions about the population. Major types of inference include regression, confidence intervals, and hypothesis tests.