To make charts color blind friendly, use colors for groups, not individual categories. This reduces the number of colors, visual clutter, and color confusion.

More color blind charts tips: hubs.ly/Q03q6TRN0

To make charts color blind friendly, use a single hue palette, which is readable for all types of color blindness, including monochromacy. Alternatively, use a red-yellow-blue palette, effective for all but monochromacy.

Read more about charts for color blind: hubs.ly/Q03q6TRN0

To make charts accessible for color-blind users, consider using color-blind friendly palettes and adding strokes around elements to enhance distinction.

More about charts for color blind: hubs.ly/Q03q6TRN0

To make charts color blind friendly, use alternatives like dashed lines and varying stroke thicknesses for line charts and their variations.

More about charts for color blind: hubs.ly/Q03q6TRN0

To make charts color blind friendly, it's better to use direct labels instead of a legend, as this saves the reader's time and attention. Direct labels also help correct palettes that aren't color blind friendly.

Read more: hubs.ly/Q03q6TRN0

To make charts color blind friendly, use shapes and icons as alternatives or additions to color-coding. If colors are not visible to colorblind users, use icons to convey information alongside colors.

More about charts for color blind: hubs.ly/Q03q6TRN0

To make charts accessible for those with color vision deficiencies, use a color scheme with red and blue, avoiding red-green combinations. Adjust the saturation or brightness of these colors and add orange and yellow for variety.

Read more: hubs.ly/Q03q6TRN0

Bad data visualization: how to notice and fix it (27 cases) Learn how to identify and fix common data visualization mistakes, from chart selection to data quality issues, to ensure clear and accurate visuals.

Avoid using incorrect parameters for icon or bubble charts; only the area should represent values.

More bad chart fixes: hubs.ly/Q03mVXMP0

Bad data visualization: how to notice and fix it (27 cases) Learn how to identify and fix common data visualization mistakes, from chart selection to data quality issues, to ensure clear and accurate visuals.

Pie charts must always total 100% for accurate representation, especially in part-of-whole contexts. Rounding can cause discrepancies, but these can be corrected. If your data doesn’t naturally sum to 100%, consider using a different chart, like a bar chart.

More tips: hubs.ly/Q03mVXMP0

Pie charts represent values using sectors, judged by angle or area, which can be hard to interpret. They work best with fewer than five sectors and distinct differences. Consider grouping smaller values into an "other" category for clarity.

More bad chart fixes: hubs.ly/Q03mVXMP0

Bad data visualization: how to notice and fix it (27 cases) Learn how to identify and fix common data visualization mistakes, from chart selection to data quality issues, to ensure clear and accurate visuals.

The function of a line chart is to visualize continuous data, so using a line chart for discrete data is both strange and wrong. An alternative would be any chart that can work with discrete data, for example a bar chart.

More bad chart fixes:

Choosing the right scale for a line chart requires balancing value context and change clarity. A large scale might flatten the line, while a detailed scale may lack context. Consider using one chart for context and another zoomed-in for detail.

More tips: hubs.ly/Q03mVXMP0

Bad data visualization: how to notice and fix it (27 cases) Learn how to identify and fix common data visualization mistakes, from chart selection to data quality issues, to ensure clear and accurate visuals.

Cumulative charts aren't inherently bad but are often overused because they show an upward trend, misleading readers. A line chart showing period changes is a better practice.

More tips: hubs.ly/Q03mVXMP0

A spaghetti chart features numerous lines, making it hard to follow. To enhance clarity, create separate charts for each line or small groups (up to 4 lines). If using one chart, color all lines gray and highlight the focus line.

More bad chart fixes: hubs.ly/Q03mVXMP0

Bad data visualization: how to notice and fix it (27 cases) Learn how to identify and fix common data visualization mistakes, from chart selection to data quality issues, to ensure clear and accurate visuals.

Column charts struggle with fitting wide labels, leading to overlap, while tilting labels is inconvenient. Bar charts are a better alternative, as horizontal bars accommodate labels more effectively.

More bad chart fixes:

Bad data visualization: how to notice and fix it (27 cases) Learn how to identify and fix common data visualization mistakes, from chart selection to data quality issues, to ensure clear and accurate visuals.

Grouped bar charts can be confusing with many items and series, especially if a legend is needed. Use fewer series (under three) for clarity, or try line charts or dot plots for a cleaner look.

More bad chart fixes: hubs.ly/Q03mVXMP0

Bad data visualization: how to notice and fix it (27 cases) Learn how to identify and fix common data visualization mistakes, from chart selection to data quality issues, to ensure clear and accurate visuals.

Icons, essential in data visualization, can distort interpretation when used iso bars in bar charts by affecting height and area perception. Complex shapes further confuse area understanding. A regular bar chart is preferable.

More bad chart fixes: hubs.ly/Q03mVXMP0

Bad data visualization: how to notice and fix it (27 cases) Learn how to identify and fix common data visualization mistakes, from chart selection to data quality issues, to ensure clear and accurate visuals.

In bar charts, truncating the Y-axis to handle different value scales can distort data perception. Instead, consider using an icon chart, which represents values through area, or a treemap for a more compact and accurate data display.

More on fixing bad charts:

Data visualization involves using visual elements to present data, but numbers and labels are essential for context and meaningful charts.

More on correcting bad charts: hubs.ly/Q03mVXMP0

Handling large datasets is difficult. Scatter plots use lots of elements to represent variables, but overuse can lead to confusion. Focus on key messages and variables. Use grey for general data and color for key points.

More tips on improving charts: hubs.ly/Q03mVXMP0

Bad data visualization: how to notice and fix it (27 cases) Learn how to identify and fix common data visualization mistakes, from chart selection to data quality issues, to ensure clear and accurate visuals.

Charts should not be placed side by side if it might imply they share the same scale or axis. Place charts on top of each other if they share the horizontal axis, and side by side if they have the same vertical axis.

More on fixing bad charts: hubs.ly/Q03mVXMP0

Double axes on charts can confuse readers due to different scales and units. To prevent this, use separate charts or scatter plots for correlation.

Learn more about avoiding bad charts. hubs.ly/Q03mVXMP0

Bad data visualization: how to notice and fix it (27 cases) Learn how to identify and fix common data visualization mistakes, from chart selection to data quality issues, to ensure clear and accurate visuals.

3D charts may look appealing, but they often distort proportions, making category comparison difficult. They are also hard to read due to the distance between categories. The simple solution is to use plain charts instead.

More on bad charts and how to fix them: hubs.ly/Q03mVXMP0

Bad data visualization: how to notice and fix it (with examples) Learn how to identify and fix common data visualization mistakes, from chart selection to data quality issues, to ensure clear and accurate visuals.

Treemaps and icon charts are less effective for comparing similar values due to difficulty in interpreting area sizes, while bar charts are better for comparing values of the same order.

More on fixing bad charts: hubs.ly/Q03mVXMP0

Bad data visualization: how to notice and fix it (with examples) Learn how to identify and fix common data visualization mistakes, from chart selection to data quality issues, to ensure clear and accurate visuals.

Bar charts aren't ideal for time series because they can be cluttered and starting at zero obscures trends.. Line charts are better for showing trends with many data points, whereas bar charts work well for fewer points.

More on fixing bad charts: hubs.ly/Q03mVXMP0

Multicolored bar charts are often criticized as they add confusion without providing extra information. It's more effective to use a single color for bar charts, highlighting specific bars to emphasize particular details.

Learn more: hubs.ly/Q03mVXMP0

The topic of bad charts and data visualization is vast and unique, making it challenging to categorize every instance. We've updated an article on this subject but would appreciate feedback or examples of cases we might have missed.

Read it here: hubs.ly/Q03mVXMP0

The production of static charts is noticeably cheaper than interactive charts. A static chart can easily be made by one data visualization designer. Also, there are a lot of dataviz tools for different needs.

More about static charts: hubs.ly/Q03mbkx30

Illustrations and examples clarify ideas, facts, and arguments. Static charts, used to illustrate points, need careful preparation to be explanatory. After data collection and cleaning, the information should be distilled into a single main idea for the audience.

Read more: hubs.ly/Q03mbkx30

Society learns through stories by using a three-act narrative: setup with title and legend, confrontation in the chart, and resolution with details. Design principles helps to create a narrative akin to comic book storytelling.

More about static charts: hubs.ly/Q03mbkx30