Welcome to the Northwestern Oklahoma State University Regional Science Fair

Since 1957, NWOSU has had the honor of hosting the Northwest Oklahoma Regional Science Fair. This program has evolved over the years to become one of outreach, service learning, and has served as a springboard for aspiring students.

Our fair services the northwest most counties of Oklahoma and is a feeder fair for the Oklahoma State Science & Engineering Fair (OSSEF) held annually in Stillwater, OK at OSU. At the regional fair, judges have the capability of advancing a project to the International Science and Engineering Fair (ISEF).

**Parents**, **Teachers**, and **Students!**

Please note that since 2014, NWOSU scholarships have been awarded to the top finishing seniors at the regional fair!

- NWOSU Regional Science Fair Scholarships
- 1st Place Senior: $1,500
- 2nd Place Senior: $1,000
- 3rd Place Senior: $500

- $1,500 NWOSU Scholarships are also awarded to selected top finishing Juniors and Seniors at OSSEF in the areas of
- Biochemistry, Medicine, and Health Sciences
- Physical Science
- Zoology and Botany
- Mathematics and Computer Science

Please browse the tabs near the top of this page for detailed information about the science fair process.

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Thank you for taking on a science fair project! We hope to see you at our fair! This page is to help you do your best with your science fair project.

You can also browse the Teacher's and Judge's Main Pages for additional tips.

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Typical Components of a Science Fair Project/Board | Some Terminology (Advanced) | |

Some Terminology (The Basics) |

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Typical Components Reported on a Science Fair Project/Board

Not all of these are required -- some may not be appropriate for your particular project.

And, your teacher may require some items not listed here; that's OK!

- Title
- Clever titles are ok -- just don't let it distract from the level of your time and effortin the project

- Hypothesis
- A concise statement about what you are testing in your experiment

- Research Question
- This often guides the formation of the hypothesis; it could be the question you asked which caused you to begin looking into setting up your project

- Probelm Statement
- Usually for engineering projects, this outlines what challenge or task at hand

- Background Information
- A summarized overview of work previously done that is related to your experiment

- Materials
- Bulleted lists work well here--just identify the equipment used for the experiment

- Procedure
- Not every single step is required, but all of the critical steps would be included here

- Diagram/Photos
- For equipment that not everyone may be familiar with, diagrams and/or photos can be helpful

- Data Table
- Not all of your data need to be displayed on the board (it might not all fit!), but summarized data tables can be helpful to you during the interview

- Data Analysis
- Not every computation needs to be written out and displayed, but you should indicate what you've done to the data to formally test your hypothesis

- Graph(s) of Data
- When possible, graphs representing your data and/or data analyses should be displayed; whether hand-drawn printout out, be prepared to answer questions about your graph

- Conclusion
- Report your results here and indicate whether or not your hypothesis was supported by your observations and analyses

- Discussion
- This is a place you should identify limits of your study, things you might do differently, findings that were unexpected, or ideas for further study

- Acknowledgements/References
- Recognize those who helped you and the sources you used for your project

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This is stuff you should look into! Knowing what these things are and applying them to your project (as appropriate) will help your project be more competitive. Keep in mind, however, that not all of these will apply to every project.

*Green items in italics*refer to specific use of terminology that will likely catch the attention of judges in a**favorable**way.*Red items in italics*refer to specific use of terminology that will likely catch the attention of judges in an**unfavorable**way.

**Average** (or **Mean**)

The average of a set of values is a basic way to report a summary of that particular data set. It's pretty easy to determine. Add up all the values to get the sum. Divide this sum by the number of values that were added together. If you are recording a lot of measurements but only need to show a single value represnetative of your data, determining the average is something you should consider. Average calculator: http://www.calculatorsoup.com/calculators/statistics/average.php

- Example: You measure the heights of everyone in 5th grade, 6th grade, 7th grade, and 8th grade. One way to report the result is to find the average height in each grade. These averages could be reported in a data table of averages or a bar chart.

**Bar Chart**

**Control Group **(see **Experimental Control**)

**Experimental Control** (or **Control Group**)

In order to determine if the end result occurs due to a treatment (or somthing that was changed), you'll need to compare it to data where there was no treatment.

- Example: You are wanting to test if spraying growing oats with water in addition to regular watering will promote healthier growth than just watering the soil. To complete this test, you really should have a container of oats that you are watering and spraying with water and you should also have one container of oats that you are watering but NOT spraying with water. The second container is your control group because it is not receiving the treatment (sprayed water).

**Experimental Group**

**Dependent Variable**

**Histogram**

**Hypothesis **(see **Scientific Hypothesis**)

**Independent Variable**

**Mean **(see **Average**)

**Median**

**Mode**

**Null Hypothesis** (in statistics, the symbol *μ*_{0} is used)

In a nutshell, this is a statement of what should happen if there is no effect on what you were looking at. *A statement of the null hypothesis on project boards* is rarely done, but can be very helpful for judges. It usually corresponds to nothing changing. Usually, but not always, we hope that our reults can be used to *reject the null hypothesis*.

- Example: Your sister puts an ice cube out on the sidewalk. The null hypothesis is that the ice cube will not change (so it won't melt). If the ice cube melts, we reject the null hypothesis. If the ice cube does not melt, then we
*fail to reject tht null hypothesis*.

What are the reasons we accept or reject the null? Those are usually implied by the scientific hypothesis!

**Population (N)**

For a study involving humans or living organisms, this refers to all of possible subjects that could ever be included in the study.

- Example: There are 3,109,101 teachers in the United States, so
*N =*3,109,101. If you are doing a study on teachers, this is the total possible population.

**Qualitative Data**

**Quantitative Data**

**Sample Size (n) **Sometimes sources and websites use capital N

For a study involving humans or living organisms, this refers to the portion of the total population included in your study. Determining how large a sample size is needed for a study depends on the nature of the scientific hypothesis.

- Example: There are 3,109,101 teachers in the United States, so N = 3,109,101. A study you might be working on includes only 20 teachers, so
*n =*20. Avoid saying things like "*the population size for this study was ______.*" Instead, say "*my sample size was _______.*"

**Scatter Plot**

**Scientific Hypothesis**

Many people say this is an "if - then statement" or an educated guess. Like, "if my sister places an ice cube on the sidewalk, then it will melt." However, this is an incomplete view of what a scientific hypothesis is--and it is more than just a guess! Well defined scientific hypotheses are more developed than if-then statements, demonstrating a person's rationale for the expected outcome. It is OK and often helpful to begin a hypothesis with a brief informative statement.

- Example: In below freezing temperatures, ice can be caused to melt by chemical additives (like rock salt). I hypothesize that that adding dark colored sand on top of road ice is more effective than adding rock salt.
- The null hypothesis in this case is that rock salt is just as effective as adding dark colored sand (no difference between the two). Our hope here is that our data will reject the null hypothesis, as this will be
*evidence supporitng our hypothesis*. - If we fail to reject the null hypothesis, then our
*hypothesis is not supported by the data*.

- The null hypothesis in this case is that rock salt is just as effective as adding dark colored sand (no difference between the two). Our hope here is that our data will reject the null hypothesis, as this will be

If a hypothesis is not suported, this is OK! This is actually progress!! Scientists learn by discovering what doesn't work more often than being right. Avoid saying *my hypothesis was proven*. Technically, scientific hypotheses (and theories) can never be proven. They can only be disproven or supported by evidence.

**Test Groups**

**Treatment**

**Trials**

**Uncontrolled Variables **or **Mediating Factors**

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*Green items in italics*refer to specific use of terminology that will likely catch the attention of judges in a**favorable**way.*Red items in italics*refer to specific use of terminology that will likely catch the attention of judges in an**unfavorable**way.

**1-Tailed **and **2-Tailed Test**

When completing a statistical test on two or more sets of data, two ways you can look for a difference include "directional" and "non-directional." These are facy ways of saying "I know which value will be greater" and "The difference could go either way." If you know which value will be greater, then you choose p = 0.05 to establish statistical significance--this is a 1-tailed test. If the difference could go either way, then you choose p = 0.01 to establish statistical significance--this is a 2-tailed test.

- Example 1: You give students a quiz on spiders before watching a video on spiders. After watching the video on spiders, you give the same quiz on spiders. You know that the quiz after watching the spider video should have a greater average than the quiz given before watching the video. So you would choose a 1-tailed test and p = 0.05.
- Example 2: You give students a quiz on spiders after watching a video on spiders. Half of the students are boys and half of the students are girls. You aren't sure which group will have the greater average. So you would choose a 2-tailed test and p = 0.01.

**Correlation** or **Correlation Coefficient**** **(*r*)

If two variables are correlated, then as one increases, the other increases. Or, as one increases the other decreases. The magnitude of *r* ranges from 0.000 to 1.000. A correlation of 1.000 is very strong. Correlations can often be noticed by just looking at a scatter plot of the data, but the value itself is determined using statistics. More detailed information: https://www.mathsisfun.com/data/correlation.html . Correlation coefficient calculator: http://www.alcula.com/calculators/statistics/correlation-coefficient/

- Example: The number of people attending a movie is very strongly correlated to the number of Twizzler packages sold.
- Notes
- A strong correlation (
*r*close to 1.000) does not mean cause and effect, but it can be an indicator of such a relationship - Determining and reporting
*r*is a good start for competitive projects. However, by itself, scientists do not consider correlation coefficients as a definitive value to draw conclusions from.

- A strong correlation (

**p-value **(usually the p is lower case)

Congratulations! If you are interested in this definition, your project is likely in the top 10% of projects of the state!! This is a number generated by completing a statistical test that determines whether or not there is a statistically significant difference between two sets of data. That is a mouthful, we know. But basically, if your p-value is less than 0.05 or 0.01 (see **1-tailed and 2-tailed test**), then the statistics done to determine the p-value of your data indicate the sets of data are different and that the difference is not due to random chance. In other words, you can reject the null hypothesis.

- Example: 9 boys and 11 girls complete a quiz after watching a video. The boys' average score is 15.7. The girls' average score is 15.9. These two averages are different numerical values, but they are not statistically significantly different. In fact, the p-value is p = 0.8558. The null cannot be rejected in this case (the null would be that there is not a difference between the two test groups).
- If you want to recreate the test, the scores are as follows
- Boys: 12,19,20,13,15,17,15,16,14
- Girls: 14,18,20,15,14,15,15,15,13,18,18
- Average calculator: http://www.calculatorsoup.com/calculators/statistics/average.php
- Standard Error of Measurement (SEM) calculator: http://www.miniwebtool.com/standard-error-calculator/
- t-test calculator: https://www.graphpad.com/quickcalcs/ttest1/?Format=SEM

- Based on the p-value of your statistical test and whether it's a 1-tailed or 2-tailed test, your results will be statistically significant or not statistically significant. Statistical tests are never m
*arginally significant, slightly significant,*or r*eally significant*

**Statistical Significance** (see also **p-value**)

Strictly speaking, statistical significance and p-value aren't exactly the same things, but they are often used interchangebly. For certain p-values of statistical tests, one can conclude that there exists a statistically significant difference between data sets. For this to actually be the case, the data must meet some basic criteria. For the level of middle school and high school science fair projects, we won't go further into details that distinguish these here. If you are interested to learn more, then you can begin by looking up "statistical power," "sample data assumptions," and "sample data assumption tests."

**Statistical Test**

When you have numerical data that corresponds to a control group and an experimental group; or you have two separate experimental groups and want to see if they're really different, statistical tests can help you do this. There are LOTS of statistial tests that can be performed on data sets. Usually, doing a "t-test," "paired t-test," or "chi-squared" test is approprate for science fair projects in high school.

- Example: 9 boys and 11 girls complete a quiz after watching a video. The boys' average score is 15.7. The girls' average schore is 15.9. Although these numbers are different, there may or may not be a statistically significant difference between them.
- Notes:
- Statistical tests are not trend lines or function fits to scatter plots of data.
- Other tests include linear regression, multiple regression, ANOVA, ANCOVA, . . . these are pretty advanced and may be necessary for fairly complex hypotheses or multple treatements.

Thank you for your efforts in helping students experience "Science" by providing them with this opportunity!!

You should have received an email outlining the steps for registering science fair projects. If you need this information sent to you again, please email Steve Maier at sjmaier@nwosu.edu. This file has been uploaded to the system as well. Regional Science Fair Registration 2019 Version 1.pdf

Please note that are entering our third year using this system; thank you for your patience as we continue to refine the system! This is the system adopted by OSSEF for all regional fairs and the state fair system as well. While it shows much promise for streamlining the ISEF paperwork submission process and pooling data, there has been a learning curve :). We thank you in advance for your patience and welcome your feedback!

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Teachers Always Ask . . .

and

Judges Always Talk About . . .

"What makes a science fair project stand out from the rest?"

**The Student, Of Course!**- This is first and foremost! It's ok if students are shy-- judges anticipate nervousness and anxiousness. And students don't have to be in a suit and tie or formal dress. What judges are really hoping to see are students who open up and shine with in their interviews; so encourage them to tell their science fair project story. Not as a memorized speech, but as a conversation about something that they are excited about!

**It's Not the Project Board. "What?!"**- A well-organized and attractive project board is great, make no mistake about it. However, the interview is really key. If the student can explain everything that's on the board (and more), this is more influential in judges' decisions than the board alone. A project with hand-drawn graphs and hand-written content can, and has, placed at our regional fair.

**Is The Project "Science"?**- Many times, great projects come from ideas of others -- or books, or the web. And that is fine. However, projects that are the most competitive are those that go beyond what's already been done and already known. Otherwise, they're less of a "science" fair project and more like a demonstration. Bottom line: it's ok to go to sciencebuddies.com or Pinterest to get ideas. But encourage your students to go beyond what's already out there--it's more meaningful and fun, because it's new!

**Data and Data Analyses -- Judges are Data Nerds . . .**- To engage in science is to make observations, organize those observations in a way that helps expose patterns, and then attempt to draw conclusions from the strength of the data and the analyes completed. Even if the "data" are interviews and not numerical (like temperature recordings), these observations can be organized and/or quantified in ways that help scientists draw conclusions. In that light, if a project has data to report consider the following, because judges look for these:
- Data table (minimum)
- Plotting the data (think carefully about an appropriate type)
- Statistics (not just mean, median, and mode -- look into statistical tests for significance)
- Statements regarding possible limitations of the data (sample size, uncertainty, error bars, uncontrolled variables, etc.)

- To engage in science is to make observations, organize those observations in a way that helps expose patterns, and then attempt to draw conclusions from the strength of the data and the analyes completed. Even if the "data" are interviews and not numerical (like temperature recordings), these observations can be organized and/or quantified in ways that help scientists draw conclusions. In that light, if a project has data to report consider the following, because judges look for these:
**Genuine Interest and Respect for the Scientific Process**- This really goes back to the first item of this list: The Student. Students who are interested in and take ownership of the scientific process make favorable impressions on judges. Even if a student doesn't win her/his category at the regional fair, judges may advance them to state competition if they see that "glint" in the eye of the student presenting that convinces them the student will take full advantage of the experience.

Teachers and Students could benefit from reviewing these as well

**For Starters:**

- Please dress for the occasion; "snappy" or "business" casual are appropriate. It's ok to be fashionble, but nothing too revealing or distracting should be worn. Our advice: Consider what expectations you might have for how the students should dress. Then consider
*their*expectations of*your*dress. - Most students will be eager to share their projects. Our advice: Build on this by letting them speak to you about what they've done. Help them communicate their ideas with you by making them feel comfortable.
- Most students will be nervous. Even if they appear well-prepared and professional, they are likely anxious about how well they are going to do and what kinds of questions you'll ask them. Our advice: Break the ice with entry-level questions and build up to more challenging and technical questions.
- Use your clipboard and judging sheets wisely. Make sure students aren't able to find out results by taking a glance at what you've written. Our advice: Cover the judging sheet you are using with another unused sheet of paper. That way your comments and marks can be easily concealed.

**During the Interview:**

- If students are speaking/reciting from a rehearsed dialogue, you might politely interject with a question and try to redirect them into having a conversation about their project. Our advice: A good time to do this is if they have paused on a term or referring to the board. Interrupting them abruptly or mid-sentence could prevent them from thinking clearly to answer your questions.
- Some projects may be demonstrations or commonly completed investigations/labs. While this may yield lower marks for creativity, please remain attentive and ask questions about their project and efforts. Our advice: Find out whether or not the project was new
*to them*. If it was, this speaks more strongly about their efforts than carrying out a procedure for which they knew ahead of time what the results would be. - Please keep in mind at all times the age/grade level of the student completing the project. A project that might be more straight forward for a high school physics student might be advanced for an eith grader. Our advice: Ask the student what science class they are currently taking and if they arrived at the idea for the project for class. See if they've gone over related content in class yet or not.
- Every project must be judged. While time is always of the essence, you may find yourself spending a bit more/less time with some projects than others. Provided the difference isn't too disproportionate, this is fine. Our advice: Always be mindful of the time. You should know the number of projects you need to judge and the time that remains. If you want, jot a note down about a specific project and plan to visit with them after all projects are judged. Or, use your judging sheet to write additional suggestions/tips should the student advance to state/ISEF competition.
- Avoid leading questions that provide students with information that could be used to "Wow" the next set of judges. In our experience, students are
*extremely*perceptive of the questions they are asked, and usually work comments made by previous judges into their narratives for the next set of judges. Our advice: Once the interview is over and you are certain another judging group is not going to interview the student, you can share some insights--just be mindful of the time.

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