Science Fair Tips for Students

Note: Technical terms related to scientific exploration and statistics can be tricky! If you’re stuck on a word, look for a definition in the Organisation of Economic Co-operation and Development’s (OECD) Glossary of Statistical Terms.

  • The first step is to decide on a question to answer (your objective). Once you have a research question in mind, talk to your teacher and, if possible, to experts, about what types of data you should collect and analyze. If looking for inspiration online, be sure to verify your sources. Think about how you’ll use the results (data) before you begin your data collection.
  • A good hypothesis guides the project with a statistical question. To answer a statistical question, you have to collect data (or, observations from a population).
  • If doing an experiment, it is impossible to control every aspect of an experiment, so the hypothesis helps focus on the aspects that are most important to control.
  • Next, turn your research question into an experiment, survey or other appropriate data collection method with formal hypotheses. This step—designing the data collection—is the hardest step in a science fair project. Work closely with your teacher or advisor on this step—and if possible, a statistician.
  • When planning your project, be careful and specific in defining your focus populations. For example, if you want to see how late students stay up, what students are you interested in? Your class, the entire school, all students in your grade in the country? Honing this target population will help focus the project.
  • Consider what data you need to collect: What information is needed to answer the question asked in your hypothesis?
  • Next, consider how that data might vary. For example, as you become more familiar with your project, you will naturally get better at observing or processing your samples. What else might impact the results? Your teacher or a statistician can help you with this.
  • Do not collect all of one treatment group at once; instead, rotate your data collection randomly across all your treatment groups, so your improvement does not affect one treatment over another. (Treatment groups are the groups you are comparing within the project.)
  • Keep a daily log of everything related to your project. This important log should include everything from, “I forgot to water the plants today,” to “A tornado destroyed part of the plants I was experimenting on,” (it’s actually happened during a national award-winning project!). A form or table should capture all the information you need—include a column for comments so you can record any extra information there, or anything else irregular that you observe.
  • Would the results of the project be exactly the same if it were repeated? If the answer is yes, consult with your teacher or adviser to make sure this isn’t an issue, given the project’s hypotheses and purpose.
  • It is impossible to control every factor that will affect your project results. Focus on doing what you can do to ensure that what you are testing will not systematically vary—the rest should be randomly assigned to minimize the impact to the results.
  • Remember, the way a science fair project is designed and executed is more important than the results.
  • When assessing the data from your project, look at appropriate summary statistics such as the mean, median, mode, standard deviation, confidence intervals and correlations.
  • Don’t wait until you are creating your report and presentation to incorporate visualizations. Use them in your analysis to explore the data graphically, in as many ways as possible. For example, review it in tables, line plots (also called dot plots), histograms (bar charts), stem and leaf displays, boxplots (also called box and whisker displays), scatterplots and regression lines.
  • While reviewing the visualizations, make note of the ones that offer the best representations of your conclusions.
  • Visualizations of the data can help sort out the variables in a study and determine the type of analysis that is most appropriate.
  • Identify the variable type(s):
    • Binary (has only 2 values, e.g., YES versus NO)
    • Categorical
    • Ordinal
    • Continuous

For discussion of these terms, view this resource.

  • Are there bad observations (often called outliers) that need to be deleted? If you delete any bad observations, be up front with the judges about this and include an explanation in the project log as to which observations were deleted and why. Include all the work done at this stage in your logbook.
  • If using Excel to create visualizations, use the “Charts” tool (in the “Insert” section of the main bar). There are a number of alternate tools for this, some of which are free.
  • Also in Excel, be mindful of differences in statistical language compared to Excel descriptors. For example, what Excel calls a “histogram” is not a histogram in the true sense—it’s a bar chart. Be sure to use the correct terms when labeling visualizations.
  • Let the visualizations speak for themselves—3D renderings or other flourishes on graphs tend to make them harder to read.

It will be advantageous to complete some sections of the report before beginning the project:

  • Introduction: Think about the hypothesis of your project and research it. Compile a summary of the insights you find from this process, citing your sources.
  • Methods: Include a data collection form or table. Consider, what else should be included? What methods would improve your project?
  • Results: You can’t write this section yet, of course, but creating blank data tables and graphs as templates to come back to when you have results will help you think through the project’s design and execution.
  • Good writing makes good thinking. Plan plenty of time for your project so you can start drafting early and have time for multiple drafts. Reviewing the report from beginning to end several times will help clarify and hone the messaging. You might be surprised at the ideas that come to you as you work!

Once these sections of the report are complete, perform the data collection.

  • Enter data into the data forms as it is collected. That way, if you realize something is wrong, you have time to correct it.
  • After, return to the data and explore it the graphs.
  • Complete the assessment and conclusion sections of the report.

Then, give yourself sufficient time to draft the full report.

  • Be sure to explain the steps you went through and conclusions in terms that non-statisticians and non-scientists can understand. This includes the methods that you used, why you used them and your interpretation of the results, in the context of your hypotheses.
  • Plan to spend as much time revising and proofreading the report as you invested in writing the initial draft.
  • Check to be sure the report thoroughly and clearly addresses all key components of a science fair project.
  • Ask your teacher and a few friends to review the report. Pay attention to their feedback!
  • Are your results statistically significant? Be prepared to talk to judges about the role of change, randomness and error play in the results, including, for example, the meaning of any p-values and confidence intervals used.