descriptives

Descriptives, short for descriptive statistics, are numerical summaries that describe the main features of a dataset. They provide a concise overview of the data without drawing conclusions about a larger population. Common descriptive measures include measures of central tendency—mean, median, and mode—and measures of variability—range, variance, and standard deviation. Additional descriptors include the interquartile range, minimum and maximum values, and measures of distribution shape such as skewness and kurtosis. For ordinal or rank data, medians and percentiles are often more informative than mean values.

Descriptive statistics can be computed for different data types. Numerical data yield means and standard deviations (or medians and interquartile ranges when distributions are skewed). Categorical data are summarized by frequencies and proportions. Descriptive analysis typically accompanies data visualization, such as histograms or box plots, to convey distributional properties.

Interpretation involves describing the data set as it is, noting central tendencies, spread, presence of outliers, and any notable patterns. Descriptives are exploratory tools and do not, by themselves, support causal inferences or generalize beyond the observed data. They are often a first step in data analysis, guiding further inferential statistics and modeling. Limitations include sensitivity to outliers, dependence on the scale of measurement, and potential misinterpretation if the data are not representative of the population of interest.