Sample Tables

Tables used in papers can be so simple that they are “informal” enough to be a sentence member and not require a caption, or they can be complex enough that they require spreadsheets spanning several pages. A table’s fundamental purpose should always be to visually simplify complex material, in particular when the table is designed to help the reader identify trends. Here, a simple table and a complex table are used to demonstrate how tables help writers to record and “visualize” information and data.

Simple Table

The simple table that follows, from a student’s progress report to his advisor, represents how tables need not always be about data presentation. Here the rows and columns simply make it easy for the writer to present the necessary information with efficiency. This unnumbered and informal table, in effect, explains itself.

Plan for Weekly Progress for the Remainder of the Semester

Week of 11/28

Contact Dr. Berinni for relevant literature suggestions.

Read lit reviews from Vibrational Spectroscopy.

Research experimental methods used to test polyurethanes, including

infrared (IR) spectroscopy and nuclear magnetic resonance (NMR).
Week of 12/5

Define specific ways that polyurethanes can be improved.

Develop experimental plan.
Week of 12/12

Create visual aids, depicting chemical reactions and experimental setups.

Prepare draft of analytical report.
Week of 12/18 Turn in copy of preliminary analytical report, to be expanded upon next semester.

Complex Table

The following sample table is excerpted from a student’s senior thesis about tests conducted on Pennsylvania coal. Note the specificity of the table’s caption. Also note the level of discussion following the table, and how the writer uses the data from the table to move toward an explanation of the trends that the table reveals.

Table 1 summarizes the results of borehole dilution testing and slug testing on wells B2, B3, and B4. The hydraulic conductivities computed from the borehole dilution test velocities for these wells range over an order of magnitude—a reasonable spread for hydraulic properties of close, but varying, test sites.

Table 1. Water velocities and hydraulic conductivities of the Lower Kittanning coal at the Kaufmann site in Clearfield County, Pennsylvania, from slug tests, November 1991, and borehole dilution (BD) tests, November 1992.

 Well #

Velocity:

BD tests

Hydraulic

Conductivity:

BD tests (pe = 0.01)

Hydraulic

Conductivity:

BD tests (pe = 0.05)

Hydraulic

Conductivity:

slug tests
B2 0.054 ft/d 1.1 x 10-2 ft/d 0.054 ft/d 0.19 ft/d
B3 0.32 ft/d 0.07 ft/d 0.32 ft/d 8.9 x 10-3 ft/d
B4 0.06 ft/d 1.2 x 10-2 ft/d 6.0 x 10-2 ft/d 2.8 x 10-2 ft/d

The hydraulic conductivities computed from the borehole dilution test velocities are significantly lower than those predicted by Huang and Sheltons’ core analysis of Middle Kittanning coal. As shown in Table 1, the borehole dilution hydraulic conductivity values for wells B2 and B4 agree reasonably well with their corresponding slug test values, assuming an effective porosity between 0.01 and 0.05. This effective porosity seems high, but the dip of bedding in the study area is opposite the regional dip. This implies slumping, which could increase the effective porosity significantly.

Above, we see how the table caption and supporting text are tightly intertwined, with the aim of describing a trend. In this case, the writer compares her table data to the work of other authors and extracts key test results and quantities from the table to discuss in the paper’s text. Here, we learn that “wells B2 and B4 agree reasonably well with their corresponding slug test values,” and we can retreat to the table for verification if we wish. Put simply, the table records the facts; the writer selects from the facts to interpret the trend.