Data visualizations should be simple and easy to understand, avoiding unnecessary complexity and clutter.

The primary goal of a data visualization is to convey information clearly and efficiently, enabling users to understand the data and make informed decisions.

A pie chart with a hidden wedge is an example of a misleading data visualization because it intentionally omits a portion of the data, distorting the representation of the data.

A bar chart is the most appropriate chart type for comparing multiple data sets because it allows for easy visual comparison of the values.

Using bright colors, adding annotations, and enlarging the data points are all effective ways to highlight important data points in a visualization, drawing attention to them and making them easier to identify.

12pt is the recommended font size for labels in a data visualization because it is large enough to be easily readable while still maintaining a clean and uncluttered appearance.

Using colors that are consistent with the data helps to convey the meaning of the data more effectively and avoid misinterpretation.

A legend serves multiple purposes in a data visualization, including explaining the meaning of the data, providing additional information about the data, and identifying the different data sets in the visualization.

All of the options are best practices for designing a dashboard, as they help to create a cohesive and effective visualization tool.

16:9 is the recommended aspect ratio for a data visualization because it is the most common aspect ratio used for computer monitors and projectors.

All of the options are best practices for presenting data in a table, as they help to make the data more readable and easier to understand.

The primary purpose of a title in a data visualization is to attract attention to the visualization and encourage viewers to explore the data.

The choice of scale for the axes in a scatter plot depends on the nature of the data and the desired representation. All of the options can be appropriate in different situations.

The recommended number of data points to include in a scatter plot is between 10 and 50. This allows for a clear representation of the data while avoiding overcrowding and clutter.

All of the options are best practices for designing a heat map, as they help to create a clear and informative visualization.