Biology Report

WOU Biology 100 Series Graphs Overview

Making a graph is one of the easiest ways to get an idea of the patterns in your data. Graphing is a fairly straightforward process, but there are a few things to keep in mind.

1. Type of graph. You should think carefully about the kind of data you have before you decide what type of graph to produce. See Figure 1. a. Line graphs are useful to show how a factor changes over time or in some other

gradual continuous increment (like temperature or ambient light). b. Bar graphs are useful to show a total change or overall difference between

different discrete variables (like types of organisms or specific experimental treatments).

Figure 1. Types of Graphs. The graph on the left is a line graph. The graph on the right is a bar graph.

2. Variables

a. The independent variable is the variable that you change or manipulate in the experiment. This variable is usually placed along the x (horizontal) axis. In the case of an experiment where you are observing something that changes over time, time serves as an independent variable and is always listed on the x-axis. If, in addition to time, there is a second independent variable (e.g. observing what happens to two different treatments over time) this variable is usually graphed by drawing multiple lines on the graph. See Figure 2.

b. The dependent variable is the response or what happens in response to the independent variable. Typically, this variable is what you counted or measured during the experiment. This variable is placed along the y (vertical) axis.

3. Titles and Labeling.

a. Every graph needs a concise and descriptive title that explains what phenomenon the graph is attempting to visualize. If you averaged data from several different lab groups before graphing, you should note in the title that your graph depicts averaged data (like in the bar graph in Figure 1).

b. Each axis should be labeled, and the label should include the units in which the data was recorded. Without units, your graph is meaningless.

WOU Biology 100 Series Graphs Overview

Table 1, below, shows an example of data collected during an experiment. The same data is presented in Figure 2. Note how much easier it is to quickly examine the patterns of data collected in the visual graph compared to the data table, as long as the graph is titled properly, the axes are labeled (with units) and there is a key.

Table 1: Data table showing gas generation (viewed as movement of liquid up a tube) by Elodea plants under different conditions. Note use of units in the table headings.

Movement of liquid in tube (in centimeters) Time (minutes) Clear test tube Foil covered test tube

5 0.7 0 10 1.1 0.2 15 1.4 0.3 20 1.7 0.4 25 2.1 0.4 30 2.8 0.4 35 3.6 0.4 40 4.5 0.4 45 5.8 0.4 50 6.7 0.4 55 7.6 0.4 60 8.8 0.4

Figure 2: A line graph with title, labels (including units), and a key. This data is the same as the data provided in Table 1.

4. Keys. If your graph includes multiple variables (see Figure 2), it is necessary to include a key. While you may find it useful to color-code your graph, remember that not all printers or copiers produce color. Thus, the use of symbols (like the diamonds and squares at each data point in Figure 2) and gray-scale in keys is most appropriate to ensure that someone trying to interpret your graph can do, even in black and white.

WOU Biology 100 Series Graphs Overview

5. Scale. It is important to choose the appropriate scale for each axis. Figure 2 shows the appropriate scale for oxygen generation by Elodea in light. See Figure 3 for inappropriate scales. To determine the appropriate scale, it is usually best to examine the maximum and minimum data points, and then choose a scale that will allow you to show those points at either end of the axis. a. A scale that is too large will compress your data points, and will not allow you to

see the relevant patterns in the data. b. A scale that is too small will limit the amount of data you are able to present and

will also appear too busy and be hard-to-read. c. Remember also that your scale should be consistent- The y-axis in Figure 2 does

not suddenly change from increments of 5 cm to increments of 20 cm, for example.

Figure 3A. This graph has a vertical scale that is too large.

Figure 3 B. This graph has a vertical scale that is too small.