Data Table Design and Practice
Integrated Science 1
Name
Period
In the previous handout, we used the Design Outline to organize and identify the components of a scientific
experiment. In this handout, we will look at some standard methods for collecting and recording data.
Data Collection Guidelines

Information collected during an experiment is called data. Always be careful, thorough, consistent and accurate when you
are collecting data.

Always complete as many repeated trials as is reasonably possible.

Clearly identify the category of the independent variable.
 Continuous – levels of the I.V. that are measurements based on a continuous metric or English scale
 Discontinuous – levels of the I.V. that are distinct, discrete categories

Clearly identify the category of data collected for the dependent variable.
 Quantitative measurement –data collected using measurements based on standard scale of metric or English units.
 Qualitative measurement –data that is classified into discrete categories.
Data Table Design
To design and make a proper data table, you must first fully understand the data you will be collecting. Begin by identifying the
following:
a.
b.
c.
d.
Independent Variable and levels of the Independent Variable
Dependent Variable(s)
The number of trials for each level of the I.V.
Any and all calculations used to analyze the data
Now you are ready to construct your data table! Always sketch a rough draft of your data table first. Include all appropriate units.
1.
Make a table containing vertical columns for (1) the independent variable, (2) dependent variable and (3) statistical
calculations (i.e. mean). Use a ruler.
2.
Subdivide the column for the dependent variable to reflect the number of trials.
3.
Under the I.V. column, list the levels (treatments) of the independent variable in rows – preferably from the smallest to the
largest.
4.
Title the table with Independent Variable vs. Dependent Variable
5.
Record all data values and calculations in the appropriate locations!
Sample Data Table – Bean Plants and Compost Experiment
Age of Compost vs. Mean Plant Height
Age of Compost
Height of Plants (D.V) at 30
(I.V.)
Days (cm)
Trials
1
No Compost
3 Month Old Compost
6 Month Old Compost
2
3
4
5
Mean Plant
Height (cm)
Data Table Practice
Directions:
1. Consider the following scenarios below. For each scenario, identify the aspects of the Design
Outlines (previously completed) that are needed to construct appropriate data tables.
2. Using a ruler, neatly construct a blank data table for each scenario.
Scenario 1: Hole-y cow!
Susie wondered if the height of a hole punched in the side of quart-size milk carton would affect how far
from the container a liquid would squirt when the carton was full of liquid. She used 4 identical cartons
and punched the same size hole in each. The hole was placed at a different height on one side of each of the
containers. The height of the holes varied in increments of 5 cm, ranging from 5 cm to 20 cm from the base
of the carton. She put her finger over the holes and filled the cartons to a height of 25 cm with a liquid.
When each carton was filled to the proper level, she placed the carton in the sink and removed her finger.
Susie measured how far away from the carton's base the liquid had squirted. Susie repeated this procedure
3 times for each carton. Each carton was identical, and she only used tap water for each trial.
Important Aspects of Design Outline:
Independent Variable:
_
Levels (treatments) of I.V.: _______________________________________________________________
Number of trials for each level (treatment):
Quantitative Dependent Variable:
_
Scenario 1 Data Table:
Scenario 2: Let there be snow!
Randy is a very experienced snowboarder who has been sponsored by Burton for 3 years and gets free
decks and bindings from them each year. Randy loves his snowboard and wants to find a brand of wax
that will ensure him optimum performance during races. Randy decided to test 4 brands of wax, labeled
Wax A, Wax B, Wax C, and Wax D. To begin, he applied one thin coat of wax brand A to his board and
rode a chairlift up to the top of Squaw Valley. He measured a 500 meter course and rode his board straight
down the hill without turning or slowing down. He then recorded the time in seconds it took to reach the
finish line. Randy repeated his experiment ten times for each different type of wax.
Important Aspects of Design Outline:
Independent Variable:
_
Levels (treatments) of I.V.: _______________________________________________________________
Number of trials for each level (treatment):
Quantitative Dependent Variable:
_
Scenario 2 Data Table:
Scenario 3: I Love Popcorn!
Jason wanted to find out how he could increase the number of popcorn kernels that pop when cooked. He
hypothesized that more kernels would pop if he used more oil. To test this, he placed 100 kernels of “Pop
Rite’ popcorn into a pan that contained different amounts of oil. He used the following amounts of oil: 5
ml, 10 ml, 20 ml, and 30ml. He used 5 ml of oil as the control, since that is what the bag recommended. He
conducted the test 3 times for each amount of oil. He then counted the number of kernels that had popped.
Each time he heated the oil for 2 minutes and cooked the popcorn for 4 minutes.
Important Aspects of Design Outline:
Independent Variable:
_
Levels (treatments) of I.V.: ________________________________________________________________
Number of trials for each level (treatment):
Dependent Variable:
_
Scenario 3 Data Table: