Grouped frequency distribution is particularly appropriate in several scenarios, primarily when dealing with large datasets or when the data spans a wide range of values. Here are the key situations where grouped frequency distribution is beneficial:

1. Large Datasets: When the dataset contains a substantial number of unique values, using ungrouped frequency distribution can become unwieldy and impractical. Grouping the data into intervals simplifies the analysis and makes it easier to interpret trends and patterns.
2. Continuous Data: For continuous variables, such as height or weight, grouping data into ranges (e.g., 150-160 cm, 161-170 cm) allows for a more manageable representation. This helps in visualizing the distribution and identifying where most data points fall without losing significant information.
3. Identifying Trends: Grouped frequency distributions facilitate the identification of trends and patterns within the data. By condensing information into classes, researchers can quickly assess how frequently certain ranges occur, which is particularly useful in exploratory data analysis.
4. Statistical Analysis: In statistical applications where summary statistics (like mean, median, mode) are required, grouped data can provide a clearer overview while still allowing for calculations based on class midpoints.
5. Data Presentation: When presenting data to an audience or stakeholders, grouped frequency distributions can enhance clarity and comprehension. They allow for effective visualizations, such as histograms, that convey information succinctly.