Final answer:
Exploratory visualizations are used to understand data and discover insights during analysis, while explanatory visualizations are designed to communicate a specific insight or story derived from data to an audience. Both play critical roles in data analysis and communication but serve different purposes. It's also important to critically assess visualizations due to their inherent selective representation of data.
Step-by-step explanation:
The difference between an exploratory visualization and an explanatory visualization lies in their purpose and usage within data analysis and communication. Exploratory visualizations are used when analyzing data to understand underlying patterns, trends, and insights. These visualizations are meant for data scientists or analysts who are familiar with the data and are trying to find out what the data can tell them. They tend to be more open-ended, allowing for the discovery of unexpected insights, and do not necessarily come with a preconceived notion of what the data might reveal.
In contrast, explanatory visualizations are created with the intention of conveying a specific message or insight that has been derived from the data to an audience. These visualizations are more refined and targeted, often simplifying the data to a key takeaway or conclusion that is meant to be easily understood by viewers regardless of their prior knowledge. The aim of an explanatory visualization is to communicate a story clearly and effectively, typically recommending a specific solution or shedding light on a particular issue.
It is important to recognize that any graph or visualization represents a selective view of data, influenced by the choices made in its design including what data is included, how it is processed, and how it is presented. As such, visualizations should be approached with a critical eye. For instance, an analysis of economic models could be supported by different types of graphs depending on what is being communicated: a line graph could illustrate the relationship between two variables over time, while a bar graph might compare different entities, and a pie chart could show how a total amount is divided among different categories.