**Descriptive statistics**are used to describe the basic features of the data in a study. They provide simple summaries about the sample and the measures. Together with simple graphics analysis, they form the basis of virtually every quantitative analysis of data. Various techniques that are commonly used are classified as:

In general, statistical data can be described as a list of

*subjects*or

*units*and the data associated with each of them. Although most research uses many data types for each

*unit*, we will limit ourselves to just one data item each for this simple introduction.

We have two objectives for our summary:

When we are summarizing a quantity like length or weight or age, it is common to answer the first question with the

**arithmetic mean,**the

**median,**or the

**mode.**Sometimes, we choose specific values from the cumulative distribution function called quantiles.

The most common measures of variability for quantitative data are the variance; its square root, the standard deviation; the range; interquartile range; and the average absolute deviation (average deviation).

Graphical description in which we use graphs to summarize data.

Tabular description in which we use tables to summarize data.

Summary statistics in which we calculate certain values to summarize data.

We want to choose a statistic that shows how different

*units*seem similar. Statistical textbooks call the solution to this objective, a

*measure of central tendency.*

We want to choose another statistic that shows how they differ. This kind of statistic is often called a

*measure of statistical variability*.

**Steps in descriptive statistics**

Collect data

Classify data

Summarize data

Present data

Proceed to inferential statistics if there are enough data to draw a conclusion.

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