Graphing and Data Interpretation

Learning Objectives

Identify the key components of a good scientific graph (title, axes labels, units).

Create a line graph to show the relationship between an independent and dependent variable.

Interpret the trend shown in a graph (increasing, decreasing, no change).

Distinguish between correlation and causation.

A Picture of the Data

After an experiment, a scientist is left with a set of data, usually in a table. While tables are useful for recording data, they can make it hard to see patterns or trends. The best way to visualize the relationship between your variables is by creating a graph. A graph is a visual representation of data.

The most common type of graph in science is a line graph. Line graphs are used to show how a dependent variable changes in response to a change in the an independent variable.

Components of a Good Graph

Every good scientific graph needs a few key components to be clear and easy to read. A common acronym to remember these is TAILS:

T - Title: The title should be descriptive and explain what the graph is about. It often takes the form of 'The Effect of the [Independent Variable] on the [Dependent Variable]'.

A - Axes: The graph must have two axes, the x-axis and the y-axis.

Independent Variable is always plotted on the horizontal x-axis.

Dependent Variable is always plotted on the vertical y-axis.

I - Intervals: The numbers on the axes must be at consistent, even intervals (e.g., counting by 2s, 5s, 10s, etc.). The scale should be chosen to make the graph fill most of the available space.

L - Labels: Each axis must have a label telling you what variable is being shown.

S - Scale: The label for each axis must also include the units of measurement in parentheses, e.g., 'Temperature (°C)' or 'Time (seconds)'.

Interpreting a Graph

Once a graph is created, you can analyze it to find the relationship, or trend, between the variables.

Increasing Trend (Positive Correlation): As the independent variable (x) increases, the dependent variable (y) also increases. The line on the graph goes up from left to right.

Decreasing Trend (Negative Correlation): As the independent variable (x) increases, the dependent variable (y) decreases. The line on the graph goes down from left to right.

No Trend (No Correlation): As the independent variable (x) increases, the dependent variable (y) stays about the same. The line is flat or the points are scattered randomly.

Correlation vs. Causation

This is a very important idea in science. A correlation is a relationship where two variables change together. Causation is when a change in one variable causes a change in another.

A correlation does not prove causation!

Classic Example: Over the summer, ice cream sales and the number of shark attacks are both strongly correlated—they both increase. Does this mean eating ice cream causes shark attacks? Of course not. There is a third, 'hidden' variable: hot weather. Hot weather causes more people to buy ice cream AND causes more people to go swimming, which leads to more shark encounters.

A well-designed experiment with controlled variables helps to establish causation, but simply observing a correlation is not enough.

Key Terms

**Graph: A diagram showing the relation between variable quantities, typically of two variables, each measured along one of a pair of axes at right angles.
**Line Graph: A type of graph used to display information that changes continuously over time or in response to another variable.
**Independent Variable: The variable that is manipulated by the experimenter, plotted on the x-axis.
**Dependent Variable: The variable that is measured in response, plotted on the y-axis.
**Trend: The general direction in which something is developing or changing, as seen in a graph.
**Correlation: A mutual relationship or connection between two or more things. It does not automatically mean one causes the other.