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"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? thinking what nobody has thought."

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 thinking what nobody has thought."

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 thinking what nobody has thought."

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 thinking what nobody has thought."

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 thinking what nobody has thought."1,2,3 & 4

Step #1 - Record Your Data CompletelyThe 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, thinking what nobody has thought."2,3 & 4

Step #2 - Organize Your Data in a Master SpreadsheetThe 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, thinking what nobody has thought."3 & 4

Step #3 - Create Sub-spreadsheetsAfter 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 & thinking what nobody has thought."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 thinking what nobody has thought."

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 thinking what nobody has thought."

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 thinking what nobody has thought."

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 thinking what nobody has thought."

- 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 thinking what nobody has thought."

2012 Science Fair Professional Development Series

Selecting Types of Graphs thinking what nobody has thought."

- 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 sub-type that draws a line.)

2012 Science Fair Professional Development Series

Selecting Types of Graphs thinking what nobody has thought."

- 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 thinking what nobody has thought."

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 thinking what nobody has thought."

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 thinking what nobody has thought."

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 thinking what nobody has thought."

Systematic errors result from flaws in the procedure.

Consider a battery testingexperiment 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 thinking what nobody has thought."

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 thinking what nobody has thought."

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 thinking what nobody has thought."

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 thinking what nobody has thought."

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 thinking what nobody has thought."

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 thinking what nobody has thought."

- 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 thinking what nobody has thought."

"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

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