Science Fair Helper

Reminder: 8th Grade Science does not offer any extra credit during the year.

The only way to earn extra credit in science class is by participating in the Arcola Science Fair. You could receive up to 5% points.

Writing Your Abstract

An abstract is an abbreviated version of your science fair project final report. For most science fairs it is limited to a maximum of 250 words (check the rules for your competition). The science fair project abstract appears at the beginning of the report as well as on your display board.

Almost all scientists and engineers agree that an abstract should have the following five pieces:

· Introduction. This is where you describe the purpose for doing your science fair project or invention. Why should anyone care about the work you did? You have to tell them why. Did you explain something that should cause people to change the way they go about their daily business? If you made an invention or developed a new procedure how is it better, faster, or cheaper than what is already out there? Motivate the reader to finish the abstract and read the entire paper or display board.

· Problem Statement. Identify the problem you solved or the hypothesis you investigated.

· Procedures. What was your approach for investigating the problem? Don't go into detail about materials unless they were critical to your success. Do describe the most important variables if you have room.

· Results. What answer did you obtain? Be specific and use numbers to describe your results. Do not use vague terms like "most" or "some."

· Conclusions. State what your science fair project or invention contributes to the area you worked in. Did you meet your objectives? For an engineering project state whether you met your design criteria.

Things to Avoid

· Avoid any technical terms that most readers won't understand.

· Avoid abbreviations or acronyms Abstracts do not have a bibliography or citations.

· Abstracts do not contain tables or graphs.

· If you are working with a scientist or mentor, your abstract should only include procedures done by you, and you should not put acknowledgements to anyone in your abstract.


Graphing Your Science Data

Different types of graphs are appropriate for different experiments. These are just a few of the possible types of graphs:

A bar graph is best used for comparing data quickly and easily. It can be used to compare different trials or different experimental groups. It also may be a good choice if your independent variable is not numerical. (In Microsoft Excel, generate bar graphs by choosing chart types "Column" or "Bar.")

A time-series plot or line graph is be used if your dependent variable is numerical and your 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 your choice of x-axis units, make a separate data column that contains those units next to your dependent variable. Then choose the "XY (scatter)" chart type, with a sub-type that draws a line.).

An scatter plot shows the relationship between your dependent and independent variables when both are numerical and the dependent variable is a function of the independent variable.

A scatter plot might be the proper graph if you're trying to show how two variables may be related to one another.

A pie graph would be appropriate for displaying percentages


  • Look at your data. Do you have enough (at least 3 trials?)
  • Did you make any mistakes
  • Calculate an average for the different trials (if appropriate)
  • Clearly label all tables with the units of measurement (volts, grams, centimeters, etc)
  • Place your independent variable on the x-axis and the dependent variable on the y-axis

What Makes for a Good Graph?

For a Good Graph, You Should Answer "Yes" to Every Question

Have you selected the appropriate graph type for the data you are displaying?

Yes / No

Does your graph have a title?

Yes / No

Have you placed the independent variable on the x-axis and the dependent variable on the y-axis?

Yes / No

Have you labeled the axes correctly and specified the units of measurement?

Yes / No

Does your graph have the proper scale (the appropriate high and low values on the axes)?

Yes / No

Is your data plotted correctly and clearly?

Yes / No

Displaying Your Data

Congratulations! You have collected data and have results from at least 3 trials of your experiment. How do you go from raw data to summaries that help you analyze data and support your conclusions? Descriptive statistics can help you to summarize mathematical data. You can use measures of central tendency, mean, median, and mode to convey information about your data.

Measures of Central Tendency: Mean, Median, and Mode

In most cases, the first thing that you will want to know about a group of measurements is the "average." But what, exactly, is the "average?" Is it the mathematical average of our measurements? Is it a kind of half-way point in our data set? Is it the outcome that happened most frequently? Actually, any of these three measures could conceivably be used to convey the central tendency of the data. Most often, the mathematical average or mean of the data is used, but two other measures, the median and mode are also sometimes used.


The mean value is what we typically call the "average." You calculate the mean by adding up all of the measurements in a group and then dividing by the number of measurements. For the "without compost" case, the mean is 5, as you can see in the illustration below.

Median and Mode

The easiest way to find the median and the mode is to first sort each group of measurements in order, from the smallest to the largest

Mean, Median, or Mode: Which Measure Should I Use?

In general, the mean is the descriptive statistic most often used to describe the central tendency of a group of measurements. Of the three measures, it is the most sensitive measurement, because its value always reflects the contributions of each of the data values in the group.

On the other hand, sometimes it is an advantage to have a measure of central tendency that is less sensitive to changes in the extremes of the data. For example, if your data set contains a small number of outliers at one extreme, the median may be a better measure of the central tendency of the data than the mean.

If your results involve categories instead of continuous numbers, then the best measure of central tendency will probably be the most frequent outcome (the mode). For example, imagine that you conducted a survey on the most effective way to quit smoking. A reasonable measure of the central tendency of your results would be the method that works most frequently, as determined from your survey.

Let’s Plan!

Do you have at least 3 trials of data?

Which of the 3 measures of central tendency (mean, median, or mode) do you plan to use?



  • Look at your data. Do you have enough (at least 3 trials?)
  • Did you make any mistakes
  • Calculate an average for the different trials (if appropriate)
  • Clearly label all tables with the units of measurement (volts, grams, centimeters, etc)

Data Analysis Checklist

What Makes for a Good Data Analysis Chart?

For a Good Chart, You Should Answer "Yes" to Every Question

Is there sufficient data to know whether your hypothesis is correct?

Yes / No

Is your data accurate?

Yes / No

Have you summarized your data with an average, if appropriate?

Yes / No

Does your chart specify units of measurement for all data?

Yes / No

Have you verified that all calculations (if any) are correct?

Yes / No

How to Write a Conclusion

Essentially the conclusion is a report on "what happened?" It summarizes your experimental experiences and correlates it with the research you did. If someone were to read just one page about what the experiment was about, how it turned out, and what information to draw from it then this would be it. It should be clear, concise and stick to the point.

  1. Begin by summarizing the purpose of your experiment
  2. Explain your hypothesis and whether or not the experiment supported your hypothesis
  3. Explain your procedures/research methods
  4. Look at your results and write a paragraph summarizing them. Make sure to cite data from your experiment
  5. Explain what (if anything) you would do differently if you had to repeat the project (would you change a variable?)
  6. Explain any applications that your results have for

Here is a sample conclusion from

Results (see for information on graphing

According to my experiments, the Energizer maintained its voltage (dependent variable) for approximately a 3% longer period of time (independent variable) than Duracell in a low current drain device. For a medium drain device, the Energizer maintained its voltage for approximately 10% longer than Duracell. For a high drain device, the Energizer maintained its voltage for approximately 29% longer than Duracell. Basically, the Energizer performs with increasing superiority, the higher the current drain of the device.

The heavy-duty non-alkaline batteries do not maintain their voltage as long as either alkaline battery at any level of current drain.


My hypothesis was that Energizer would last the longest in all of the devices tested. My results do support my hypothesis.

I think the tests I did went smoothly and I had no problems, except for the fact that the batteries recover some of their voltage if they are not running in something. Therefore, I had to take the measurements quickly.

An interesting future study might involve testing the batteries at different temperatures to simulate actual usage in very cold or very hot conditions.