Descriptive Statistics: Summarizing and Understanding Data

Descriptive statistics contain some of the more well-known statistical measures, such as mean, median, mode, and variability. They form the foundation of statistical analysis. Before a researcher can answer questions about student outcomes, assessment types, or curriculum directions, they will need to analyze the descriptive statistical measures. For example, as technology advances and new tools are constantly introduced, will tutor-marked assessments have different outcomes than computer-marked assessments? Before attempting to answer these types of educational questions, researchers must first organize the data and examine the key descriptive measures to reveal what is typical and how much variability exists (Gravetter et al., 2021). Central tendency provides a simple and accessible set of measures to compare data sets, while measures of dispersion help quantify consistency among or within the data (Gravetter et al., 2021). The shape of the data adds context, which could potentially reveal outliers and/or explore how well the data points cluster together (Gaciu, 2025). Descriptive statistics help make sense of raw data on a very basic level, which then allows further inferential analysis to take place en route to impacting educational technology research (Mertler, 2019).