Continuous Data — Definition, Formula & Examples
Continuous data is data that can take any numerical value within a range, including decimals and fractions. Examples include height, weight, temperature, and time — measurements that don't jump from one value to the next but flow smoothly across a scale.
Continuous data is a type of quantitative data drawn from a variable whose possible values form an unbroken interval on the real number line. Between any two recorded values, infinitely many other values are theoretically possible, and the precision of the data is limited only by the measuring instrument.
How It Works
Continuous data comes from measuring rather than counting. When you measure a person's height, the result could be 152 cm, 152.4 cm, or 152.413 cm — any value is possible depending on how precise your ruler is. Because continuous data can take infinitely many values, it is typically displayed using histograms, line graphs, or box plots rather than bar charts with gaps. When you collect continuous data, you often group the values into intervals (called bins or classes) to summarize and analyze them.
Worked Example
Problem: A science class records the temperatures (°C) of water samples from five different locations: 18.3, 20.7, 19.45, 22.1, and 17.86. Organize these values into a frequency table using 2-degree intervals starting at 16.
Step 1: Identify the range. The lowest value is 17.86 and the highest is 22.1.
Step 2: Set up intervals (bins) of width 2, starting at 16: 16–18, 18–20, 20–22, 22–24.
Step 3: Tally each value into its interval. 17.86 falls in 16–18 (1 value). 18.3 and 19.45 fall in 18–20 (2 values). 20.7 falls in 20–22 (1 value). 22.1 falls in 22–24 (1 value).
Answer: The frequency table has four intervals: 16–18 (frequency 1), 18–20 (frequency 2), 20–22 (frequency 1), and 22–24 (frequency 1). Notice the data points can land anywhere inside an interval because temperature is continuous.
Visualization
Why It Matters
Understanding continuous data is essential in middle-school and high-school statistics courses, where you need to choose the right type of graph and summary statistic for your data. In careers like engineering, medicine, and environmental science, nearly all measurements — blood pressure, rainfall, voltage — produce continuous data. Recognizing data as continuous also prepares you for more advanced topics like probability distributions and calculus-based statistics.
Common Mistakes
Mistake: Assuming data is discrete just because it's recorded as whole numbers.
Correction: Rounded measurements (like height recorded as 160 cm instead of 160.24 cm) are still continuous. The underlying variable can take any value; rounding is just a limitation of recording.
Mistake: Using a bar chart with gaps for continuous data.
Correction: A histogram (bars touching with no gaps) is the correct display because continuous data has no natural gaps between values. Gaps between bars imply separate categories.