Excel makes it easy to turn numbers into visuals, but “easy” doesn’t always mean “accurate.” Some chart types do more to hide your data than highlight it. If you want your reports to be readable and professional, stop using these confusing layouts right away.
Stop using pie charts for complex comparisons
Circular data is a recipe for confusion
We’ve all been there. You have a handful of categories, and you instinctively click that colorful circle in the Insert tab. It looks friendly, it’s classic, and it feels like the right thing to do. However, the pie chart is one of the most misused tools in the spreadsheet kit.
The fundamental issue is biological: the human brain is surprisingly bad at judging angles and comparing areas. Statisticians Cleveland and McGill’s study on graphical perception shows that we are much better at comparing the heights of two bars—”a position along a common scale”—than figuring out whether one slice is slightly larger than another. When values are close, a pie chart turns into interpretation guesswork because “it fails if the decoding process fails.”
The problem worsens as you add categories. Beyond three or four segments, you end up with a cluttered ring of slivers, requiring labels, leader lines, and a legend just to decode what you’re looking at. At that point, the visual has simply stopped communicating. Pie charts only work reasonably when you have very few categories (usually two or three) and the differences are obvious at a glance.
What to use instead: Use a bar chart when comparing categories, especially when values are close or labels are long. Use a column chart when you want to compare a few categories side by side in a simple ranked or time-based order. Their straight baselines make comparison immediate and effortless. If you still need a proportional comparison, use a sorted bar chart and display percentages directly on the bars instead of relying on angles.
Stop using 3D charts for visual depth
Depth makes your data harder to read
If pie charts are misleading, 3D charts are actively deceptive. They might look impressive, but they introduce perspective distortion that changes how data is perceived.
In a 3D column or bar chart, elements closer to the viewer appear larger than those further away, even when values are identical. Gridlines become harder to read, and exact alignment with axis values becomes unclear. You’re no longer reading data—you’re interpreting an angle. This violates a core principle of data pioneer Edward Tufte: “The number of information-carrying dimensions depicted should not exceed the number of dimensions in the data.” In practice, this means any chart element that adds visual structure beyond the data itself should be treated with caution.
This creates accidental bias. A dip can look less severe, or a spike more dramatic, simply because of how Excel renders perspective.
What to use instead: Use flat 2D charts for all standard comparisons. If you need emphasis, highlight specific data points with color, annotations, or data labels instead of adding perspective effects.
Stop using dual-axis charts for comparisons
Mixed scales lead to misleading trends
Dual-axis charts seem efficient: two datasets, one chart. In reality, they’re one of the easiest ways to imply relationships that don’t actually exist.
The problem isn’t that dual-axis charts can’t show relationships—it’s that they encourage false visual correlation when scales aren’t directly comparable. By placing two different scales on the same graph—say revenue in millions and customer satisfaction out of 10—you can visually engineer correlations simply by adjusting axis ranges. Small changes on one axis can look dramatically significant next to another series.
Even when used honestly, they are difficult to read. The viewer has to constantly check which axis applies to which line, breaking the flow of interpretation. It forces the eye to bounce between the left and right margins, creating unnecessary cognitive load.
What to use instead: Use small multiples (separate, identical charts shown side by side) when you want to compare trends using the same visual scale. Use separate charts when the datasets use different units and should be interpreted independently.
Stop using area charts for overlapping data
Overlapping series hide important values
Area charts can be effective for showing part-to-whole relationships over time, but they break down quickly when multiple overlapping series are used.
The problem is occlusion. Front layers cover what’s behind them, making smaller datasets difficult—or impossible—to see clearly. Even transparency doesn’t fully solve the issue, since overlapping colors create new, unintended shades that distort interpretation.
Stacked area charts aren’t inherently bad—they shift the focus from individual values to the combined total. But if you care about the breakdown, they stop being useful very quickly, often resulting in a chart that is visually appealing but fails to communicate accurate comparisons.
What to use instead: Use line charts when comparing multiple trends over time, especially when individual series matter equally. Only use stacked area charts when the total combined value is more important than the breakdown of each series.
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Stop using radar charts for comparisons
Radial metrics distort interpretation
Radar charts look analytical, but they’re one of the hardest chart types to interpret correctly.
They plot values along radial axes, forming a shape that’s visually compelling but mathematically difficult to compare. Humans are poor at consistently judging radial distance, making comparisons between axes unreliable even when the data is accurate.
Worse, the shape itself is misleading. A larger-looking polygon doesn’t necessarily represent higher overall values—it just reflects how values extend across different axes. This encourages false pattern recognition, and when multiple datasets are added, the chart quickly becomes unreadable, with overlapping shapes creating visual noise rather than insight.
What to use instead: Use grouped bar charts when comparing multiple variables across the same categories. Use small multiples when each metric needs to be read independently without geometric distortion.
Avoiding these chart types keeps your data readable, accurate, and easy to compare—the three things that matter most in any spreadsheet report. And ditching swanky 3D blocks doesn’t mean your reports have to be boring—once you have a clean 2D layout, you can modify traditional charts for a more tailored, professional look. For example, you can use a line chart to build a dynamic timeline, or even use graphics as the columns in column charts. The goal is clarity over decoration, so your insights stand out without visual interference.
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