Grouping to understand: how to analyze data when it comes in intervals

Up to now, we've worked with individual, ungrouped data. That is, complete lists where each value represents a specific observation. This approach is useful when the amount of data is manageable, but what happens when we have tens, hundreds, or even thousands of observations? In those cases, grouping the data into classes or intervals is not only more practical but necessary. This is where descriptive statistics for grouped data comes in. Grouping data means organizing it into ranges or class intervals, rather than listing each value separately. This allows us to see patterns more clearly and makes it easier to calculate summary measures. For example, if we have the drying times of one hundred paint samples, instead of writing out each value, we can organize the results into intervals such as: 1–2 hours, 2–3 hours, 3–4 hours, and so on, counting how many times the values ​​are repeated within each interval.