Interpreting Figures and Tables

Helping Your Students to Interpret Figures and Tables: "Step One-Step Two"

Most students have little or no experience interpreting figures and tables, and so you will have to help them do this. The "step one-step two" approach shows students a logical stepwise way to make sense of data presented in figure or table form. It compels them to slow down and not give up right away (as many are inclined to do) — or not jump to interpretations too quickly.

Students don't realize that understanding figures/tables takes a lot of time. Some think that if they don't understand the data immediately, they are stupid or will never be able to figure it out. This approach gives them the confidence they will need to continue working with data throughout your course.

This is written for students so that you can give the directions to students if you wish.

Step One (Describe)

First determine how the figure/table is set up. This is the part that everyone would agree about and is not a matter of interpretation.

What are units on the axes (for a figure) or heading of the columns (for a table)? Make sure you understand what these units mean.

g m—2 = g/m2

Pay attention to the symbols on a figure, the differences between dotted and solid lines, and so on.

Now look at the pattern in the data. For a figure with lines, what is their pattern? For instance, do they increase linearly and then level off? In a table do the numbers increase across the column? Pay attention to detail; that may be important.

At this point you should have a pretty good idea of the question addressed by the data set and the experimental design — how it was carried out.

Step Two (Interpret)

Now you are ready to interpret the data. What conclusions can you draw from the pattern that you have described? What do these results tell you about the phenomenon being studied? How do they fit into the larger picture of ecological thinking?

Interpretations may well differ from person to person; this is to be expected and makes discussions about data sets all the more interesting!