What is a potential drawback of using a heat map?

A heat map effectively visualizes data through color-coded patterns, making it useful for spotting trends and correlations in large datasets. However, a notable drawback is that it may oversimplify complex data.

This oversimplification occurs when nuanced details are masked by the aggregated color representations. For instance, subtle variations and important insights can become lost when data is condensed into broad color ranges. As a result, while viewers can appreciate general patterns at a glance, they may miss critical aspects of the data, leading to potential misinterpretations or oversights in understanding underlying trends or anomalies.

In contrast, the other options relate to different considerations. While readability may vary based on the audience and design, heat maps are designed to be visually intuitive. Advanced programming skills are usually not required to create basic heat maps, given various user-friendly tools available. Additionally, heat maps can be effectively used with small datasets, although their utility may be more apparent with larger datasets.