Which technique is useful for making data comparisons?

Histogram analysis is a powerful technique for making data comparisons because it allows for the visualization of the distribution of a dataset. By using bar graphs, histograms represent the frequency of data points within specified ranges, making it easy to observe patterns, trends, and frequencies across different categories or intervals. This visual representation facilitates quick comparisons between groups or distributions, enabling analysts to identify differences or similarities in datasets effectively.

For example, comparing the heights of two different groups of individuals could be represented using histograms, showing how many individuals fall into specified height ranges for each group. This can reveal insights about the central tendencies, variations, and shapes of the datasets.

In contrast, data encryption is focused on securing and protecting data rather than comparing it. Data normalization is primarily about scaling data to a common range, which may aid in analysis but does not inherently facilitate comparison. Random sampling methods are used to select a subset of data for analysis, which can help understand a population but does not directly compare datasets upon visual inspection.