![]() In practice, both types of measures are often used together in order to summarize the data in the most concise but complete way. Some of them give an understanding about the location of the data, others give and understanding about the dispersion of the data. ![]() Several different measures (called statistics if we are analyzing a sample) are used to summarize the data. ![]() It also helps to “understand” the data and if well presented, descriptive statistics is already a good starting point for further analyses. It allows to check the quality of the data by detecting potential outliers (i.e., data points that appear to be separated from the rest of the data), collection or encoding errors. However, in many cases it is generally better to lose some information but in return gain an overview.ĭescriptive statistics is often the first step and an important part in any statistical analysis. Of course, by summarizing data through one or several measures, some information will inevitably be lost. Descriptive statistics allow to summarize, and thus have a better overview of the data. Long series of values without any preparation or without any summary measures are often not informative due to the difficulty of recognizing any pattern in the data. To learn how to compute these measures in R, read the article “ Descriptive statistics in R”.ĭescriptive statistics (in the broad sense of the term) is a branch of statistics aiming at summarizing, describing and presenting a series of values or a dataset. This article explains how to compute the main descriptive statistics by hand and how to interpret them. ![]()
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