Best for Summarizing the distribution of a dataset
Box plots, otherwise known as box-and-whisker plots, capture a summary of the distribution of a dataset, containing the minimum, first quartile, median, third quartile, and maximum. They are useful in outlier detection and for the examination of data distributions.
Tips: Box plots are good for large datasets; of course, for small datasets where individual data points may be informative, one wouldn’t want to use them. This would come under best practices that have to be followed up to see the data effectively.
1. Simplify Your Design: Against clutter, a clean and simple graph should be made. Include features such as labels, legends, or grid lines only if necessary.
2. Wisely Use Colors: Colors should enhance your graph to be read, not a hindrance. Apply contrasting colors to tell apart data series and make sure the dimensionality of the chosen colors includes color vision deficiency accessibility.
3. Clearly Label: Axes, data points, legends—everything needs to be labeled clearly. This includes making sure there can be no mistake about what each thing represents. Check that your labels are concise but full of information.
4. Choosing the Right Scale: Ensure that your axes have appropriate scales so that all data gets equal and fair representation. Do not manipulate the scale to distort the data.
5. Provide Context: Including titles, subtitles, and captions lends context to the dataset so it is easier for the audience to understand what it holds.
6. Interactive Elements: Add things like tooltips, zoom features, and filters whenever possible especially if you are going to be presenting the data online.
