Statistical data is information collected and organized so it can be counted, measured, or compared, such as survey answers, sales figures, test scores, or sensor readings. Each value describes a member of a group, and analysis looks for patterns across the whole group rather than in a single value.
Statistical data is usually described in two ways. Quantitative data is numeric: discrete values are counts, like the number of orders, and continuous values are measurements, like temperature or delivery time. Qualitative, or categorical, data places values into groups, like product type or yes and no answers. Data is also primary when a team collects it for its own question and secondary when it comes from an existing source, such as a public census.
For example, a retailer that records each order with its date, region, product category, and total value has a statistical dataset it can summarize by month or compare across regions. The same principles apply to data used for AI: a model learns from the values it sees, so the data has to represent the cases the model will face in practice.