Dotted lines on a graph signal that the data is different in some way from the solid lines around it. The most common uses are showing predictions, marking estimated or missing data, and distinguishing one data series from another when color alone isn’t enough. Choosing the right line style isn’t just decorative; it tells your reader how much to trust each part of the chart.
Showing Forecasts and Projections
The single most common reason to switch from a solid line to a dotted or dashed one is to separate what has already happened from what you expect to happen. If your graph shows actual sales from January through June and projected sales from July through December, the projection should look visually distinct. A dotted or dashed line is the clearest way to do this. Charting tools like Qlik explicitly default to a dashed line style for time series forecasts, and you’ll see the same convention in financial reports, climate models, and population projections.
The logic is simple: solid lines represent confirmed, measured data. Dotted lines represent uncertainty. When a reader scans your chart, the change in line style is an instant visual cue that says “this part is an estimate.” Without that cue, your audience might treat a projection as fact.
Filling In Missing or Estimated Data
When your dataset has gaps, you have a few choices: leave a break in the line, drop the missing points entirely, or interpolate between the points you do have. If you interpolate, a dotted line between the known data points is the standard way to show it. The dotted segment tells the reader “we don’t have real measurements here, but this is our best guess based on the surrounding values.”
TIBCO’s charting documentation, for example, renders missing values as an interpolated dotted line connecting the plot points immediately before and after the gap. This convention appears across scientific plotting libraries and business intelligence tools. If you draw a solid line through estimated values without flagging them, you’re implying a level of certainty you don’t actually have.
Distinguishing Multiple Data Series
When your graph has two or more lines, you need a way to tell them apart. Color is the obvious first choice, but it’s not always sufficient. About 8% of men and 0.5% of women have some form of color vision deficiency, and plenty of graphs still get printed in black and white or photocopied. Accessibility guidelines from organizations like NCEAS recommend against conveying information purely through color and suggest varying texture, symbols, or line patterns as a supplement.
In practice, this means using a solid line for one series, a dashed line for another, and a dotted line for a third. Even for readers with full color vision, combining color with line style makes a busy chart easier to parse at a glance. If your graph has a legend, the line pattern shows up there too, reinforcing which series is which.
Indicating Thresholds and Reference Lines
Dotted lines frequently appear as reference markers rather than data series. A horizontal dotted line might represent an average, a target value, a safety limit, or a baseline for comparison. Because the dotted line looks lighter and less prominent than a solid data line, it sits in the visual background. Your reader’s eye follows the solid data line first, then uses the dotted reference to judge whether the data is above or below a meaningful threshold.
You’ll see this in budget vs. actual charts (dotted line for the budget, solid for actual spending), medical charts (dotted line for a normal range), and performance dashboards (dotted line for a goal or KPI target). The key principle is that the dotted line represents context, not the primary story.
Choosing Between Dotted and Dashed
Dotted lines (a series of small dots) and dashed lines (longer segments with gaps) are closely related but not identical. In most graphing contexts, they’re interchangeable for the purposes described above. However, if your chart needs three or more visually distinct styles, you can use solid, dashed, and dotted as a hierarchy. A long-dash-dot pattern adds a fourth option.
ISO 128, the international standard for technical drawings, formally defines these line elements: dots, dashes of varying length, and combinations of the two separated by gaps. While you probably don’t need to follow engineering standards for a business presentation, the underlying principle is the same. Each distinct pattern should map to a distinct meaning, and you should define that meaning in a legend.
Practical Guidelines for Your Charts
A few rules of thumb will cover most situations:
- Use solid lines for measured, confirmed data. This is your default. If you only have one series of actual data with no gaps, a solid line is all you need.
- Switch to dotted or dashed for anything uncertain. Forecasts, projections, interpolated gaps, and provisional data all benefit from a non-solid style.
- Use dotted lines for secondary reference information. Averages, targets, and thresholds should recede visually behind the main data.
- Pair line styles with color, not instead of it. Color plus pattern is more accessible than either one alone.
- Always include a legend. Even if the convention feels obvious to you, label what each line style means. The APA’s figure guidelines call for a legend positioned within the figure borders that explains all symbols used.
The underlying principle across all of these cases is the same: a dotted line tells your reader that this particular line is different from the solid ones. Whether that difference is uncertainty, estimation, a secondary role, or simply a separate category, the visual change in pattern carries meaning. Use it intentionally, label it clearly, and your graphs will communicate exactly what you intend.