What technique is commonly used to visualize the distribution of a dataset?

The histogram is a powerful technique commonly used to visualize the distribution of a dataset by displaying the frequency of data points within specified ranges or bins. It provides a clear representation of the underlying frequency distribution, allowing analysts to easily identify patterns, such as central tendency, spread, and any potential outliers.

By plotting the number of occurrences of data points within each range, a histogram effectively gives insight into how the data is distributed across different values, making it easy to see where data clusters or if there are gaps. This is particularly useful when analyzing continuous data and understanding its overall shape, whether it’s normal, skewed, or has multiple peaks.

Other visualization methods like pie charts and line graphs serve different purposes—pie charts are more suited for displaying parts of a whole, while line graphs are used to show trends over time. Box plots, on the other hand, summarize data through their quartiles and can indicate outliers, but they do not provide as immediate a sense of the actual distribution of data points as a histogram does. Thus, a histogram stands out as the most effective choice for visualizing the distribution of a dataset.