Choosing a chart should not start with a chart gallery. It should start with a question.
What does the reader need to understand? Are they comparing categories, tracking a trend, looking at a distribution, or trying to understand the relationship between two variables?
The right chart makes that answer obvious. The wrong one can hide the pattern, exaggerate differences, or leave readers wondering what they are supposed to notice.
This guide explains how to choose a data chart based on the story your data needs to tell, along with practical tips for making charts clear, accessible, and trustworthy.
Key Takeaways
Start with the question your chart needs to answer.
Use bar charts to compare categories.
Use line charts to show changes over time.
Use histograms to understand how numeric values are distributed.
Use scatter plots to explore relationships between two variables.
Use pie charts sparingly when you need to show simple part-to-whole relationships.
Keep charts readable by limiting clutter, labeling important information clearly, and using color alongside other visual cues.
Use tables when readers need to look up or analyze precise values.
Choose the simplest chart that communicates the intended message without making the reader decode it.
What Is a Data Chart?
A data chart is a visual representation of information that helps readers see patterns, comparisons, trends, or relationships.
Instead of asking readers to work through a column of numbers, a chart can make an important pattern visible at a glance. This is particularly useful when the dataset contains enough values that comparing them individually would take time.
For example:
A bar chart can show which product category has the highest sales.
A line chart can show how sales changed throughout the year.
A histogram can show how customer orders are distributed by value.
A scatter plot can show whether two measurements appear to move together.
The best chart is not necessarily the most sophisticated one. It is the one that communicates the intended answer with the least effort from the reader.
A useful visualization should also give the reader enough context to interpret the numbers correctly. Titles, units, labels, and scales all contribute to that context.
How Do You Choose the Right Chart?
Before choosing a chart, finish this sentence:
"I want my reader to understand..."
Your answer will usually point you toward the right visual.
For example, if the answer involves ranking different products, a bar chart is a natural starting point. If the answer involves how performance changed from January to December, a line chart is likely more appropriate.
This question-first approach also prevents a common problem: choosing a chart because it looks interesting and then trying to make the data fit it.
Comparing Categories: Use a Bar Chart
Use a bar chart when you want to compare separate categories.
For example, you might compare:
Sales by product
Customers by region
Website visits by traffic source
Revenue by department
Support tickets by issue type
Bar charts make differences between categories easy to see. If you have many categories, consider ordering them from highest to lowest so the comparison is easier to follow.
Horizontal bars can also work well when category names are long. They give labels more room and can prevent awkwardly rotated text.
If the categories have a natural order, such as age groups or stages in a process, preserve that order rather than automatically sorting them by value.
Showing Change Over Time: Use a Line Chart
Use a line chart when the important question is how something changes over time.
Examples include:
Monthly revenue
Daily website traffic
Annual customer growth
Weekly order volume
Quarterly operating costs
Put time along the horizontal axis and the measurement on the vertical axis. This gives readers a natural left-to-right view of change.
Line charts are especially useful when you want readers to notice the direction and pace of change rather than focus on individual values.
If you have several series, avoid creating a chart with so many lines that the reader cannot tell them apart. Direct labels, different line styles, or data points can make multiple series easier to understand.
For shorter time periods, make sure the intervals are consistent. Uneven time intervals can give a misleading impression of how quickly a value changed.
Showing Part of a Whole: Consider a Pie Chart
Pie charts can show how categories contribute to a whole.
For example, you might show how a company's total revenue is divided among five business areas.
They work best when:
The categories add up to a meaningful whole.
There are only a few categories.
The differences between categories are reasonably easy to see.
The reader needs to understand the overall composition rather than compare many precise values.
For many categories, a bar chart is usually easier to compare. If two slices are close in size, readers may also struggle to judge the difference accurately.
A good rule is to ask whether the "whole" is actually important to the story. If it is not, a simple comparison chart may communicate the information more effectively.
Showing a Distribution: Use a Histogram
A histogram shows how numeric values are distributed across ranges, or bins.
For example, a histogram could show:
Customer ages
Order values
Delivery times
Test scores
Session durations
Instead of displaying every individual value, the histogram groups values into ranges and shows how frequently each range occurs.
This makes it easier to spot patterns such as clustering, gaps, or unusually high or low values.
The choice of bin size matters. Very wide bins can hide useful patterns, while very narrow bins can make the distribution look noisy. The goal is to choose ranges that reveal the overall shape of the data without overwhelming the reader.
Exploring Relationships: Use a Scatter Plot
Use a scatter plot when you want to explore the relationship between two numeric variables.
For example:
Advertising spend and sales
Product price and demand
Study time and test scores
Website traffic and conversions
A scatter plot can reveal whether values appear to move together and whether unusual observations stand out.
You can also use a scatter plot to identify clusters. A group of points concentrated in one area may suggest that certain observations behave differently from the rest of the dataset.
However, a visible relationship does not automatically mean that one variable causes the other. Other factors may influence both variables, so treat the chart as evidence of an association, not proof of causation.
Chart Type at a Glance
| Question | Recommended chart |
|---|---|
| Which category is larger? | Bar chart |
| How has something changed over time? | Line chart |
| How does a whole break into categories? | Pie chart or bar chart |
| How are numeric values distributed? | Histogram |
| Are two variables related? | Scatter plot |
| Do readers need exact values? | Table |
Should You Use a Chart or a Table?
Charts and tables serve different purposes.
Use a chart when you want readers to see a pattern quickly. A visual can make a rising trend, large difference, or unusual value stand out immediately.
Use a table when readers need to find, compare, or work with exact numbers.
For example, a chart might show that sales increased steadily throughout the year. A table can then provide the exact sales figure for every month.
Tables are also useful when readers may want to reference the information later. If someone needs to know the exact value for March rather than simply understand the overall trend, a table is often the better format.
In many cases, using both is the strongest approach: let the chart communicate the pattern and let the table provide the detail.
How to Make a Data Chart Easier to Understand
Choosing the right chart is only the first step. The design can determine whether readers understand the information correctly.
A technically accurate chart can still fail if its labels are unclear, its scale is confusing, or its visual hierarchy makes the wrong detail stand out.
Give the Chart a Useful Title
Avoid vague titles such as:
Monthly Sales
A more useful title tells readers what they should notice:
Monthly Sales Increased Throughout the Second Half of the Year
The second title gives the reader a reason to look at the chart and provides context before they start interpreting individual values.
You do not need to turn every title into a conclusion, but the title should make the subject and purpose of the chart clear.
Remove Unnecessary Clutter
Every element on a chart should help readers understand the data.
Consider removing:
Decorative backgrounds
Excessive gridlines
Unnecessary borders
Redundant labels
Decorative 3D effects
Legends that could be replaced with direct labels
A simpler chart is often easier to read because the important information has less competition.
This does not mean removing useful context. Axis labels, units, annotations, and other explanatory elements should stay when they help readers interpret the data.
Label Important Information Clearly
Readers should not have to guess what each axis, series, or value represents.
Use:
Clear axis titles
Understandable units
Meaningful category names
Direct labels where appropriate
Consistent number formatting
If a chart uses thousands of dollars, for example, make that clear rather than leaving readers to interpret the numbers themselves.
Labels should also use language your audience understands. Avoid technical abbreviations or internal terminology unless the readers are already familiar with them.
Be Careful With Color
Color can help readers distinguish categories, but it should not be the only way information is communicated.
Some readers may have difficulty distinguishing certain colors. Use labels, patterns, shapes, line styles, or other visual differences when they can reinforce the meaning.
Also avoid using too many colors. A chart with ten competing colors can be harder to understand than one using a small, consistent palette.
Reserve strong visual emphasis for the information you actually want readers to notice. If everything is highlighted, nothing appears important.
Keep Scales Honest
Chart scales can dramatically change how a difference appears.
For bar charts, starting the value axis at zero is generally the safest approach because the bar length itself represents the value.
Line and scatter charts may sometimes use a narrower scale when doing so makes small changes easier to examine, but the scale should always be clear.
When comparing multiple charts, use consistent scales whenever possible. Otherwise, similar values can appear dramatically different simply because the charts use different axes.
Pay attention to units as well. Switching between units such as thousands and millions without making the change obvious can make an otherwise accurate chart confusing.
Common Data Chart Mistakes
Even a technically correct chart can communicate poorly.
Using a Chart That Does Not Match the Question
A chart should serve the analytical task, not the other way around.
If you need to compare categories, start with a bar chart rather than choosing a visually impressive chart that makes comparisons harder.
The same principle applies to more specialized visuals. A chart can be attractive and technically sophisticated while still being the wrong tool for the information you need to communicate.
Putting Too Much Information Into One Chart
Adding more series, categories, labels, and annotations does not necessarily make a chart more informative.
If readers need several minutes to figure out what they are looking at, consider simplifying the chart or splitting it into multiple visuals.
Sometimes two focused charts communicate a story much better than one crowded dashboard-style visual.
Relying on Color Alone
If two categories are distinguished only by red and green, some readers may not be able to tell them apart.
Add labels, patterns, shapes, or different line styles where appropriate.
This also helps readers who view charts in grayscale, print them, or encounter them on displays where colors appear differently.
Using Pie Charts for Too Many Categories
A pie chart with many similarly sized slices makes comparison difficult.
If readers need to compare several categories, a sorted bar chart will usually communicate the differences more clearly.
If you find yourself adding many labels or a complicated legend to explain a pie chart, that is often a sign that another chart type would work better.
Using Dual Axes Without a Strong Reason
Two vertical axes can make unrelated measurements appear connected or exaggerate a relationship.
If possible, use separate charts when the measures have substantially different scales or meanings.
If a dual-axis chart is necessary, make both axes and their corresponding series unmistakably clear so readers do not accidentally compare values that use different scales.
Hiding Important Context
A chart should not force readers to guess what the numbers represent.
Always consider whether the chart needs:
A time period
A unit of measurement
A data source
A definition for an unfamiliar metric
An explanation of unusual values
A note about missing or incomplete data
A little context can prevent a lot of misinterpretation.
A Simple Chart-Selection Process
You can use this five-step process whenever you need to create a new data visualization.
1. Define the question.
What does the reader need to know?
Write the question in plain language before selecting a chart. This keeps the visualization focused on the reader's needs rather than the available chart types.
2. Identify the data relationship.
Are you comparing categories, showing change over time, examining a distribution, or exploring a relationship?
Understanding the structure of the data will quickly eliminate chart types that are not suitable.
3. Choose the simplest suitable chart.
Start with a familiar chart before considering a more specialized option.
If a basic bar or line chart communicates the answer clearly, there is usually little reason to replace it with a more complicated visual.
4. Remove anything that does not help answer the question.
Simplify labels, colors, gridlines, and other visual elements.
Then look at the chart again. The main point should be easier to see after each round of simplification, not harder.
5. Check the chart from the reader's perspective.
Can someone understand the main point without your explanation?
Can they tell what the axes represent? Can they identify the important categories? Can they understand the scale?
If not, the chart probably needs another round of editing.
The Final Chart Check
Before publishing a chart, take a final pass through these questions:
Does the chart answer a specific question?
Is the chart type appropriate for the data?
Is the main takeaway easy to identify?
Are the axes and units clear?
Is the scale appropriate?
Are labels readable?
Is there unnecessary visual clutter?
Does the chart rely on color alone?
Could a table communicate the information better?
Would a reader understand the chart without additional explanation?
If the answer to these questions is yes, the visualization is likely doing its job.
The Bottom Line
The right data chart is the one that makes the intended answer easier to see.
Start with the question, identify the relationship in your data, and choose the simplest visual that communicates it clearly. Then remove unnecessary clutter, label important information, use color thoughtfully, and check that the scale does not distort the story.
A good chart does not make readers work harder to understand the data. It does the opposite: it turns numbers into an answer readers can see.
Frequently Asked Questions
What is the best chart for comparing categories?
A bar chart is usually the best starting point for comparing categories because differences in bar length are easy to see.
What chart should I use for trends?
Use a line chart when you want to show how a value changes over time. It is particularly useful when the sequence and direction of change matter.
When should I use a histogram?
Use a histogram when you want to understand how numeric observations are distributed across ranges.
When should I use a scatter plot?
Use a scatter plot when you want to explore whether two numeric variables are associated. Remember that a relationship shown in a scatter plot does not by itself establish causation.
Are pie charts still useful?
Yes, but only for relatively simple part-to-whole comparisons. When there are many categories or small differences between values, a bar chart is usually easier to interpret.
Should I use a chart or a table?
Use a chart when the goal is to communicate a pattern or comparison quickly. Use a table when readers need exact numbers or need to examine the underlying data.
How do I make a chart accessible?
Start by making the chart easy to understand without relying on color alone. Use clear labels, sufficient text contrast, distinguishable visual elements, and a scale that accurately represents the data.
What makes a data chart trustworthy?
A trustworthy chart accurately represents the underlying data and gives readers enough context to interpret it. Clear labels, honest scales, appropriate chart types, and transparent presentation all help prevent misleading conclusions.
