Continuous Data: Meaning and Chart Examples
TERMMeasurable numbers that can fall anywhere on a continuum.
Continuous data is numeric data that can take any value within a range, including fractions and decimals — height, weight, temperature, time and distance are classic examples. Between any two values another valid value always exists, so it is measured rather than counted and is best charted with histograms, line charts and scatter plots.
A person can be 170 cm tall, or 170.4 cm, or 170.43 cm; the only limit is how precise the measuring tool is. That is what makes data continuous. Compare that with the number of children in a family — you can have 2 or 3, but never 2.6 — which is discrete data.
Continuous vs discrete vs categorical data
| Type | What it is | Examples | Typical charts |
|---|---|---|---|
| Continuous | Measured; any value in a range, including decimals | Height, weight, temperature, time taken, rainfall | Histogram, line chart, scatter plot, box plot |
| Discrete | Counted; whole numbers with gaps between | Number of goals, customers per day, pets owned | Bar chart (column chart), dot plot |
| Categorical | Labels or groups, not quantities | Country, blood type, product name | Bar chart, pie chart |
A quick test: ask "does a value halfway between two observed values make sense?" Halfway between 20.1 °C and 20.2 °C is 20.15 °C — meaningful, so temperature is continuous. Halfway between 3 and 4 customers is 3.5 customers — not meaningful, so a customer count is discrete. For a fuller breakdown, including nominal, ordinal, interval and ratio scales, see data types explained.
Examples of continuous data
- Body measurements: height, weight, blood pressure, body temperature.
- Time: how long a web page takes to load, a runner's race time, hours of sleep.
- Physical quantities: distance, speed, air temperature, rainfall in millimetres.
- Prices and rates over time: a share price or exchange rate tracked continuously through the day.
Examples of charts for continuous data
Histogram
A histogram groups continuous values into bins (for example 160–165 cm, 165–170 cm) and draws touching bars showing how many values fall in each. The bars touch because the bins are adjacent ranges on a continuous scale with no gaps between them — the visual difference from a bar chart of categories.
Line chart
A line chart connects measurements taken over time. The connecting line is itself a claim that the quantity exists between the measured points: if a thermometer read 12 °C at 9 am and 16 °C at 11 am, the temperature at 10 am was somewhere in between. That is why the line suits continuous data and is misleading for unrelated categories.
Scatter plot
A scatter plot places two continuous variables against each other — say height on the x-axis and weight on the y-axis — so you can see the relationship, or correlation, between them.
Box plot
A box plot summarises a continuous variable with its median, quartiles and range, and is handy for comparing distributions across groups.
A worked example
A coach records the 100 m sprint times of 30 athletes: 11.82 s, 12.07 s, 12.31 s and so on. These times are continuous — two runners could in principle differ by a thousandth of a second. To see the overall spread, the coach puts the times in a histogram with half-second bins (11.5–12.0, 12.0–12.5, …). To track one athlete across a season, the coach uses a line chart of their time at each meet. To ask whether training hours relate to speed, the coach uses a scatter plot of hours against time. Same type of data, three questions, three charts.
Interval and ratio: two kinds of continuous scale
Continuous data is usually measured on an interval or a ratio scale. Both have equal steps between values; the difference is zero. On a ratio scale, zero means "none of it", so ratios make sense — 4 kg is twice 2 kg. On an interval scale, zero is arbitrary — 20 °C is not "twice as hot" as 10 °C, because 0 °C is not the absence of temperature. This matters for charts: bar lengths imply ratios, so bars on an interval scale like Celsius can mislead.
Treating rounded measurements as discrete (age recorded in whole years is still continuous underneath); drawing a line chart between unrelated categories, which implies values in between that do not exist; and using a bar chart with gaps for binned continuous data, which hides that the ranges are adjacent.
Related terms
Continuous data is usually plotted against a linear scale, summarised with the median and range, and grouped into bins for a histogram. Its opposite in charting terms is categorical data.
Frequently asked questions
What are examples of continuous data charts?
The most common charts for continuous data are histograms, which show the distribution in bins; line charts, which show change over time; scatter plots, which compare two continuous variables; and box plots, which summarise the spread.
What does the line in a line chart mean for continuous data?
The line connecting points says the quantity exists between the measurements. If temperature was 12 degrees at 9 am and 16 degrees at 11 am, it passed through values in between. That is why lines suit continuous data and mislead for unrelated categories.
What is the difference between continuous and discrete data?
Continuous data is measured and can take any value in a range, including decimals, such as height or time. Discrete data is counted in whole numbers with gaps between them, such as the number of goals scored.
Is age continuous or discrete data?
Age is continuous because time passes continuously, but it is often recorded in whole years, which makes it look discrete. Treat it as continuous when charting a distribution, for example in a histogram.
Why do histogram bars touch?
Histogram bars touch because each bar is a range of a continuous variable and the ranges sit next to each other with no gaps. Bar charts of categories leave gaps because the categories are separate.