To effectively categorize age data, you begin by defining appropriate age ranges, commonly referred to as “intervals” or “bins.” The selection of these intervals should be based on the context of your analysis and the level of detail required. For example, you might define intervals such as $0-9$, $10-19$, $20-29$, and so forth. In a scenario where you’re examining the age distribution of a primary school, you might opt for smaller intervals, like $0-4$, $5-9$, etc.

Once you have established your intervals, the next step is to assign each age in your dataset to the corresponding category. For instance, if your list includes ages such as $3$, $15$, $22$, and $37$, you would categorize them as follows: age $3$ would fall into the $0-9$ category, age $15$ would be placed in the $10-19$ category, age $22$ would go into the $20-29$ category, and age $37$ would fit into the $30-39$ category.

After categorizing the data, you can create a frequency table to display the number of ages that fall into each interval. This table provides a clear overview of the age distribution. For example, a frequency table might look like this: