Science Fair
Data, Data, Everywhere
2012 Science Fair Professional Development Series
"Discovery consists of seeing what everybody has seen and thinking what nobody has thought."
- Albert Szent-Gyorgyi
2012 Science Fair Professional Development Series
What’s so important about data?
The data that you record now will be the basis for your science fair
project final report and your conclusions so capture everything in
your laboratory notebook.
Remember to use numerical measurements as much as possible.
If your experiment also has qualitative data (not numerical), then
take a photo or draw a picture of what happens.
Before starting your experiment, prepare a data table so you can
quickly write down your measurements as you observe them.
2012 Science Fair Professional Development Series
Collecting Data
As you observe your experiment, you will need to record the
progress of your experiment.
Data can be whatever you observe about your experiment that
may or may not change during the time of the experimentation.
Examples of data are: pH values, temperature, a measurement of
growth, color, distance, etc.
2012 Science Fair Professional Development Series
Recording Data
All scientists keep a record of their observations in some form of a
journal or notebook.
The journal will begin with the date and time the experimenter
collects the data.
Sometimes data will include environmental values such as
humidity, temperature, etc.
Entries must be written clearly and with details of descriptions so
that another scientist can read the journal, simulate the
conditions of the experiment, and repeat the experiment exactly.
2012 Science Fair Professional Development Series
DATA
The data are the values written down as the experiment progresses.
Example of data entry on measuring plant growth:
11/15/04
Control Plant
7.4 mm
Test Plant
16.2 mm
Test Plant
24.9 mm
Test Plant
37.2 mm
2012 Science Fair Professional Development Series
Managing Data
1,2,3 & 4
Step #1 - Record Your Data Completely
The first step in organizing your science fair project data is to
record each data set completely. Recording your data needs to be
done carefully and mindfully as mistakes in recordkeeping will
impact the results of your science fair project. This can corrupt
your findings and lead you down the wrong path.
To record your data completely you will want to write legibly and
include as much information about your observations as possible.
You will also want to make sure you standardize the information
that you collect for each test subject or entry.
2012 Science Fair Professional Development Series
Managing Data 1,2,3 & 4
Step #2 - Organize Your Data in a Master Spreadsheet
The master spreadsheet will have one line for each test.
Information relating to a variety of characteristics of that test
subject will then be recorded in the columns that follow the row.
For example, if you are collecting data on a runner to see how their
caloric intake impacts their physical condition after running ten
miles you will put the name of the test subject in the far left
column, then you will record their heart rate in the next column
on the same row, followed by their rate of respiration in the next
column and their blood pressure in the following column.
2012 Science Fair Professional Development Series
Managing Data 1,2,3 & 4
Step #3 - Create Sub-spreadsheets
After you have a master spreadsheet set up your next step is to
create sub-spreadsheets that will focus on specific pieces of data.
For example, if you take the runner experiment you would have
one spread sheet that contains data that relates to heart rate,
one that relates to blood pressure and one the relates to the rate
of respiration.
2012 Science Fair Professional Development Series
Managing Data 1,2,3 &
4
Step #4 - Analyze Your Data
“looking for patterns in the data”
The final step is to analyze the data in each sub-spreadsheet.
The goal of data analysis is to determine if there is a relationship
between the independent and dependent variables. Ask yourself,
“Did the change I made have an effect that can be measured?”
This is done by using statistical analysis tools offered by the
spreadsheet program that you are working with. You can graph
your data points as well using spreadsheet program tools.
2012 Science Fair Professional Development Series
Data Analysis
There are many observations that can be made when looking at
data tables!

Comparing mean average or median numbers of objects

Observing trends of increasing or decreasing numbers

Comparing modes or numbers of items that occur most
2012 Science Fair Professional Development Series
Data Analysis
Besides analyzing data on tables or charts, graphs can be used to
make a picture of the data.
Graphing the data can often help make relationships and trends
easier to see.
Graphs are called “pictures of data.”
The important thing is that appropriate graphs are selected for the
type of data.
2012 Science Fair Professional Development Series
“Picturing” the Data
Bar graphs, pictographs, or circle graphs should be used
to represent categorical data (sometimes called “side by
side” data). Other options include: Pie Charts, X & Y
axis Graphs, Histograms, etc.
Line plots are used to show numerical data.
Line graphs should be used to show how data changes
over time.
Graphs can be drawn by hand using graph paper or
generated on the computer from spreadsheets.
2012 Science Fair Professional Development Series
Graphing Guidelines
 Generally, you should place your independent variable on the
x-axis of your graph and the dependent variable on the y-axis.
 Be sure to label the axes of your graph— don't forget to
include the units of measurement (grams, centimeters, liters,
etc.).
 If you have more than one set of data, show each series in a
different color or symbol and include a legend with clear
labels.
2012 Science Fair Professional Development Series
Example Graph
2012 Science Fair Professional Development Series
Selecting Types of Graphs
 A bar graph might be appropriate for comparing different trials
or different experimental groups. It also may be a good choice if
the independent variable is not numerical. (In Microsoft Excel,
generate bar graphs by choosing chart types "Column" or "Bar.")
 A time-series plot can be used if the dependent variable is
numerical and the independent variable is time. (In Microsoft
Excel, the "line graph" chart type generates a time series. By
default, Excel simply puts a count on the x-axis. To generate a
time series plot with a choice of x-axis units, make a separate
data column that contains those units next to the dependent
variable. Then choose the "XY (scatter)" chart type, with a subtype that draws a line.)
2012 Science Fair Professional Development Series
Selecting Types of Graphs
 An xy-line graph shows the relationship between the dependent
and independent variables when both are numerical and the
dependent variable is a function of the independent variable. (In
Microsoft Excel, choose the "XY (scatter)" chart type, and then
choose a sub-type that does draw a line.)
 A scatter plot might be the proper graph to use when trying to
show how two variables may be related to one another. (In
Microsoft Excel, choose the "XY (scatter)" chart type, and then
choose a sub-type that does not draw a line.)
2012 Science Fair Professional Development Series
Error Analysis
One frequent problem with science fair projects is the lack
of error analysis.
All scientific reports must contain a section for error analysis. The
purpose of this section is to explain how and why the results
deviate from the expectations.
Error analysis should include a calculation of how much the results
vary from expectations. This can be done by calculating the
percent error observed in the experiment.
 Percent Error = 100 x (Observed- Expected)/Expected
 Observed = Average of experimental values observed
 Expected = The value that was expected based on hypothesis
2012 Science Fair Professional Development Series
Error Analysis
After calculating the percent error observed in the experiment, the
error analysis should then mention sources of error that explain
why the results and the expectations differ.
Sources of error must be specific.
"Manual error" or "human error" are not acceptable sources of
error as they do not specify exactly what is causing the
variations. Instead, systematic errors in the procedure must be
discussed to explain such sources of error in a more rigorous
way.
2012 Science Fair Professional Development Series
Error Analysis
Once the sources of error have been identified, it must be
explained how they affected the results.
Did they make the experimental values increase or decrease?
Why?
Sources of error are classified into one of two types:
systematic error
random error
2012 Science Fair Professional Development Series
Error Analysis
Systematic errors result from flaws in the procedure.
Consider a battery testing experiment where the lifetime of a
battery is determined by measuring the amount of time it takes
for the battery to die.
A flaw in the procedure would be testing the batteries on different
electronic devices in repeated trials. Because different devices
take in different amounts of electricity, the measured time it
would take for a battery to die would be different in each trial,
resulting in error.
Because systematic errors result from flaws inherent in the
procedure, they can be eliminated by recognizing such flaws and
correcting them in the future.
2012 Science Fair Professional Development Series
Error Analysis
Random errors result from the limitations in making
measurements necessary for the experiment.
All measuring instruments are limited by how precise they are.
The precision of an instrument refers to the smallest difference
between two quantities that the instrument can recognize.
For example, the smallest markings on a normal metric ruler are
separated by 1mm. This means that the length of an object can
be measured accurately only to within 1mm. The true length of
the object might vary by almost as much as 1mm. As a result, it
is not possible to determine with certainty the exact length of the
object.
2012 Science Fair Professional Development Series
Error Analysis
Another source of random error relates to how easily the
measurement can be made.
Suppose you are trying to determine the pH of a solution using pH
paper. The pH of the solution can be determined by looking at the
color of the paper after it has been dipped in the solution.
However, determining the color on the pH paper is a qualitative
measure. Unlike a ruler or a graduated cylinder, which have
markings corresponding to a quantitative measurement, pH
paper requires that the experimenter determine the color of the
paper to make the measurement. Because people's perceptions
of qualitative things like color vary, the measurement of the pH
would also vary between people.
2012 Science Fair Professional Development Series
Error Analysis
Random error can never be eliminated because instruments
can never make measurements with absolute certainty.
However, it can be reduced by making measurements with
instruments that have better precision and instruments that
make the measuring process less qualitative.
2012 Science Fair Professional Development Series
Data Analysis Checklist
What Makes for a Good Data Analysis Chart?
For a Good Chart, Answer "Yes" to Every Question
 Is there sufficient data to know whether the hypothesis is
correct?
 Yes / No
 Is the data accurate?
 Yes / No
 Has the data been summarized with an average, if appropriate?
 Yes / No
 Does the chart specify units of measurement for all data?
 Yes / No
 Are all calculations (if any) correct?
 Yes / No
2012 Science Fair Professional Development Series
Graph Checklist
What Makes for a Good Graph?
For a Good Graph, You Should Answer "Yes" to Every Question
 Is it the appropriate graph type for the data being displayed?
 Yes / No
 Does the graph have a title?
 Yes / No
 Is the independent variable on the x-axis and the dependent
variable on the y-axis?
 Yes / No
2012 Science Fair Professional Development Series
Graph Checklist continued
 Are the axes labeled correctly with the specified the units of
measurement?
 Yes / No
 Does the graph have the proper scale (the appropriate high and
low values on the axes)?
 Yes / No
 Is the data plotted correctly and clearly?
 Yes / No
2012 Science Fair Professional Development Series
In the End
"It Didn't Work!"
A science fair experiment is not invalid if the hypothesis proves
to be false because of the hypothesis tests! Believe it or not, the
most important aspect of an experiment is how well the
scientific method is followed when doing the experiment!
Just because the hypothesis did not pan out does not mean the
experiment is without merit – quite the opposite. Some of the
best science experiments show how a seemingly obvious
hypothesis turns out to be incorrect when critically tested by
correctly using the scientific method.
2012 Science Fair Professional Development Series