Appendix A: Criteria for Graphing Scientific Data
Often the first step in analyzing the results of an experiment is the presentation of the
data in the form of a graph. A graph is a visual representation of the data, which assists
in bringing out and finding the possible relationship(s) between the independent and
dependent variables. Examination of a graph makes it much easier to see the effect the
independent variable has on the dependent variable(s).
Accurate and clearly constructed graphs will assist in the interpretation and
communication of your data, and when presenting a well-documented argument
supporting or falsifying your hypothesis in the final steps of a scientific investigation.
All graphs should be easy to interpret and labeled fully. The following guidelines will
help you construct a proper graph.
Graphing tips
1) Use graph paper of a high quality.
2) A ruler should be used to draw axes and to plot data neatly and accurately.
3) Always graph the independent variable on the x-axis (horizontal axis), and the
dependent variable on the y-axis (vertical axis).
4) The scales of the axes should be adjusted so that the graph fills the page as much
as possible. The axes often, but not always, start at zero. Choose your intervals
and scales to maximize the use of the graph paper. Intervals should be logically
spaced and easy to interpret when analyzing the graph (e.g. intervals of 1’s, 5’s, or
10’s are easily interpreted, but non-integer intervals (e.g. 3.25’s, 2.33’s, etc.) are
not. To avoid producing a graph with a lot of wasted space a discontinuous scale
is recommended for one or both scales if the first data point is a large number.
Simply add two tic marks between the zero and your lowest number on one or
both axes to show that the scale has changed.
5) Label both axes to indicate the variable and the units of measure. Write the
specific name of the variable. Do not label the axes as the dependent variable
and independent variable. Include a legend if different colors are used to indicate
different aspects of the experiment.
6) Graphs (along with drawings, and diagrams) are called figures and are numbered
consecutively throughout a lab report or scientific paper. Each figure is given a
number, a title that describes contents, and an informative sentence giving enough
information for the figure to be understandable apart from the text (e.g. Figure 1.
Temperature and Leaf Color Change. The relationship between the change in
vine maple leaf color and changes in ambient temperature). Generally, this
information is placed below the figure or graph.
7) Choose the type of graph that best presents your data. Line and bar graphs are the
most common. The choice of graph type depends on the nature of the variable
being graphed.
Line Graphs are used to graph data that only involves continuous variables. A
continuous variable is capable of having values over a continuous range (i.e. anywhere
between those that were measured in the experiment). For example, pulse rate,
temperature, time, concentration, pH, etc. are all examples of continuous variables. See
figure 1 on the next page.
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Making Line Graphs
1) Plot data as separate points. Make each point as fine as possible and then
surround each data point with a small circle. If more than one set of data is
plotted on the same graph, distinguish each set by using circles, boxes, triangles,
etc.
2) Generally, do not connect the data points dot to dot. Draw smooth curves, or if
there appears to be a linear relationship between the two variables, draw a line of
best fit.
3) If more than one set of data is plotted on a graph, provide a key of legend to
indicate identify each set. Label the graph as a figure, give it an informative title,
and a descriptive sentence.
Rate of lactose digestion
(mg lactose/min.)
20.0
15.0
10.0
5.0
0.0
1.0
2.0
3.0
4.0
5.0
6.0
7.0
8.0
9.0
pH of solution
Figure 1. pH effects on lactase. Note that a line graph was used to graph the data
because both variables, pH and the rate of digestion, are continuous variables.
Bar Graphs are used if the data involves a discrete variable (non-continuous
variable). A discrete variable, unlike a continuous variable, cannot have intermediate
values between those measured. For example, a bar graph (Figure 2, below) would be
used to plot the data in an experiment involving the determination of chlorophyll
concentration (chlorophyll concentration is a continuous variable) found in the leaves of
different tree species (The discrete variable is the species of tree). Bar graphs are
constructed using the same principles as for line graphs, except that the vertical bars are
drawn in a series along the horizontal axis (i.e. x-axis). In the example below, a bar
graph was used to graph the data because tree species is a discrete variable since it is
impossible to have a value or species between those used.
Chlorophyll concentration
(mg / gram of leaf )
8.0
7.0
6.0
5.0
4.0
3.0
2.0
1.0
0.0
Dogwood
Vine Maple
Red Alder
Figure 2. Chlorophyll Concentrations. The chlorophyll concentrations were
measured mg/grams of leaf in the leaves of three tree species.
Biology 100 Laboratory Manual
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Revised Summer 2004