are used to describe the basic features of the data
gathered from an experimental study in various ways. 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. It is necessary to be familiar with primary methods of describing data in order to understand phenomena and make intelligent decisions.
Various techniques that are commonly used are classified as:
- * Graphical displays of the data in which graphs summarize the data or facilitate comparisons.
- * Tabular description in which tables of numbers summarize the data.
- * Summary statistics (single numbers) which summarize the data.
In general, statistical data can be briefly 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, this introduction treats only the simplest case.
There may be two objectives for formulating a summary statistic:
- To choose a statistic that shows how different units seem similar. Statistical textbooks call one solution to this objective, a measure of central tendency.
- To choose another statistic that shows how they differ. This kind of statistic is often called a measure of statistical variability.
When summarizing a quantity like length or weight or age, it is common to answer the first question with the arithmetic mean, the median, or, in case of a unimodal distribution, 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).
When formulating a graphical display to summarise a dataset, the same two objectives may apply. A simple example of a graphical technique is a histogram, in which the central tendency and statistical variability can both be visualised.
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.
- Descriptive Statistics Lecture: University of Pittsburgh Supercourse: http://www.pitt.edu/~super1/lecture/lec0421/index.htm