How can you simplify visualizing continuous data without sacrificing accuracy?

1 Choose the right chart type

The first step is to choose the right chart type for your continuous data, depending on your purpose, audience, and data characteristics. For example, if you want to show the distribution of a single variable, you can use a histogram, a box plot, or a density plot. If you want to show the relationship between two variables, you can use a scatter plot, a line chart, or a regression plot. If you want to show the change of a variable over time, you can use a time series plot, a line chart, or an area chart. You can also use interactive charts, such as sliders, filters, or zooms, to allow your audience to explore the data in more detail.

2 Simplify the data

The second step is to simplify the data by reducing the noise, outliers, or complexity that might distract or confuse your audience. You can do this by applying various statistical methods, such as smoothing, binning, aggregating, or transforming the data. For example, if your data are too noisy or volatile, you can use a moving average, a loess curve, or a spline to smooth the data and show the trend. If your data have too many values or categories, you can use binning or aggregating to group the data into meaningful intervals or summaries. If your data have a skewed or non-normal distribution, you can use a logarithmic, square root, or power transformation to make the data more symmetric and easier to compare.

3 Use visual cues

The third step is to use visual cues, such as color, size, shape, or texture, to highlight the most important or interesting aspects of your continuous data. You can do this by applying various graphical elements, such as markers, lines, bars, or areas, to encode the data values and show the patterns, trends, or outliers. For example, if you want to emphasize the difference between two groups of data, you can use color to encode the group membership and contrast the colors to make them stand out. If you want to show the magnitude of the data values, you can use size to encode the value and scale the size to make it proportional. If you want to show the variability of the data values, you can use shape or texture to encode the standard deviation or confidence interval and vary the shape or texture to make it noticeable.

4 Follow design principles

The fourth step is to follow design principles, such as simplicity, clarity, consistency, and balance, to make your visualization easy to understand and aesthetically pleasing. You can do this by applying various design techniques, such as choosing a suitable color scheme, font, and layout, removing unnecessary elements, adding informative labels and titles, and aligning and spacing the elements properly. For example, if you want to choose a suitable color scheme, you can use a sequential, diverging, or categorical scheme, depending on the type and range of your data, and use a color palette generator or a color theory guide to select the colors. If you want to remove unnecessary elements, you can use the principle of data-ink ratio and eliminate the chart junk, such as grid lines, borders, or background colors, that do not add any information or value to your visualization.

By following these four steps, you can simplify visualizing continuous data without sacrificing accuracy and create effective and engaging visualizations that communicate your data story to your audience.

5 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?