Minimum Quality Requirements for Project Types

You are here: Home / Rules and Registration / Minimum Quality Requirements for Project Types

Read the Minimum Quality Requirements below as well as the ISEF Rules and Guidelines before you start your project.

As an ISEF affiliated fair, the Synopsys Championship follows ISEF Rules and uses ISEF forms although we have some additional requirements which are explained in the application process pages. 

MINIMUM QUALITY REQUIREMENTS for Projects

Find the type of project you are doing from the list below and review the Minimum Quality Requirements (MQR) for project acceptance. Make sure to use the appropriate research or engineering plan and that it contains the required information. Detailed templates for research plans and engineering plans are linked below and in the Required Forms page.

Types of Science Fair Projects

Science Project: Design and conduct an experiment to test a hypothesis.
Engineering Project: Design, build and test a solution to address a specific problem or need.
Human Participants Project: Uses humans to test something; requires a Human-Participants Research Plan in addition to your Science or Engineering plan. 
Data Analysis/Machine learning Project: Analyze large datasets to identify useful patterns or build computer models.
Product Testing Project: (grades 6-8 only) Quantitatively test and compare items.
Demonstration Project: These are not accepted.
Literature reviews: These are not accepted.

MQR for each project type

Science Project MQR

This is what most people think of as a science fair project. Science projects must use the Science Project Detailed Research Plan template. Science projects begin by defining a testable question. Such questions usually begin with Why… or What is the effect of a change in X on Y? (for example, what is the effect of a change in the amount of sunlight on the growth of tomato plants). Once the testable question has been selected science projects require the following. 

  1. Bibliography including sufficient properly cited references to show how your proposal is connected to previous research in the field. A minimum of 3 quality references are required for middle school projects and 5 for high school projects.
  2. Hypothesis based on your library research and knowledge. It is your best estimate of how what you measure will change as a result of changes to your test variable and what those changes mean. NOTE that testing to see if a prototype or model works is NOT a hypothesis.
  3. General methods Explain the basic idea of what you are doing. Define the quantitative endpoint(s): what are you measuring and how? How would the results support or disprove your hypothesis?
  4. Materials list List all materials you plan on using in your experiment including reagents and cleaning materials. Materials should then be listed on your other forms, especially form 3 as appropriate.
  5. Experimental design/detailed methods
    • Describe what you will be doing in enough detail that someone else could conduct your experiment following these instructions.
    • Define a control (a “standard” group) to which all test groups will be compared.
    • Define several test groups with different conditions of the one variable that will be changed; you will compare these to the “control” group.
    • Each test should be conducted a minimum of 3 times, testing should be simultaneous wherever possible.
    • Plan to change only one variable in each test. However, change the variable in several ways (several concentrations of a chemical, several temperatures, or several time points etc.).
    • Report measurements in appropriate scientific units.
  6. Data collection describing what data you expect to collect and how.

Engineering Project MQR

Instead of testing a hypothesis, engineering projects involve the development of innovations and solutions that address problems or needs using the design-build-test process. Computer models are an example of an engineering project. Engineering projects must use the Engineering Project Detailed Research Plan template.

  1. Define the need or problem in a Project Goal statement.
  2. Cite appropriate scholarly published research and include a Rationale statement.
  3. Specify at least two design criteria for middle school and three design criteria for high school as well as any design constraints.
  4. Design criteria are parameters that reflect the design performance, and are usually measurable. Examples: Speed, elasticity, energy use, efficiency, and accuracy.
  5. Design constraints are factors that limit variability in the design and engineering work, and usually are expressed as constants or a fixed range of values. Examples: size, weight, shape, materials, and budget.
  6. Provide a materials list that includes all materials and equipment used in the project.
  7. Provide relevant figures including drawings, schematics, maps, and flow charts.
  8. Provide relevant algorithms and formulae. 
  9. Describe in detail any relevant fabrication, programming and assembly work. 
  10. List at least one test plan per design criteria. Each test plan should be a step-by-step sequence of instructions that includes set-up, test execution, measurement, and the success criteria. Tests should be run a minimum of 3 times and results should be recorded in metric units where feasible.

Human Participants Projects

If you are developing an app, model, or device that will be tested by people other than yourself, you must follow the rules for human participants below. These projects require SRC/IRB pre-approval. SCVSEFA no longer accepts Behavioral Science projects involving humans, i.e. projects that look at the effects of various things on human participants. 

A Human-Participants Research Plan (see below) is required in addition to your Research or Engineering Plan for any project that directly involves humans other than the individuals submitting the work. Detailed ISEF guidelines are available from the ISEF website.

  1. Use the Human Participants Research Plan.
  2. Include a sample Informed Consent Form containing your proposed testing for SRC review.
  3. Subjects may not be asked to ingest foods without proper medical supervision and/or as a reward for participation.
  4. Have an appropriate number of participants and clear measurement criteria. 
  5. Participants may not be given rewards for participation (grades, prizes, money, food, etc).

Note: Any project that involves a physical or mental health condition that is not conducted under the direction of a qualified, medically certified supervisor may not claim to diagnose or treat a medical or mental health condition.  Apps or models designed for this purpose may not be tested by volunteers or family members or made publicly available. Projects, models and apps may claim to have the “POTENTIAL” for therapeutic effectiveness or clinical diagnostics only.

Data Analysis/AI Project Additional MQR

Projects where students didn’t generate the original data but are mining data set(s) to either reveal unique insights or develop a useful model are considered data analysis projects. Machine learning or AI is often used for this purpose. Such projects may either be a science project or an engineering project depending on whether they are testing a hypothesis or building a model. These projects must therefore follow the appropriate minimum quality requirements stated above, as well as the additional requirements below. Data analysis projects using data from a single experiment conducted by other scientist(s) are considered team projects where the team includes the individual(s) conducting the experiment and the data analyst. As such, these projects are subject to the rules governing team projects (see information regarding teams and eligibility).

  1. Data source(s) must be clearly indicated and traceable to the original publication. If data is not publicly available, the project is considered an RRI project. Any medical data must be de-identified prior to use.
  2. A rationale for the AI application in the project including possible algorithm/model choice(s) must be included.
  3. Plans for data curation/exclusion must be clearly described.
  4. Data analysis must provide unique insights. 
  5. A design plan describing how model parameters will be developed including possible boundary conditions.
  6. A validation plan using different data than that used to build the algorithm or model must be included (splitting a dataset is permissible but must be clearly stated). 
  7. Testing of at least two of the following five performance criteria:
    1. Accuracy and performance metrics
    2. Generalizability and robustness
    3. Interpretability and explainability
    4. Fairness and bias
    5. Scalability and efficiency

Product Testing Project minimum requirements [Grades 6 -8 only] 

  1. Use the Product Testing Plan template.
  2. Clearly identify what kind of item (soap, fabric, etc.) you plan to test.
  3. Define a test group of at least three (Grades 6 and 7) or four (Grade 8) similar items.
  4. Include test criteria that:
    • Define what will be measured.
    • Describe how you will take measurements.
    • Report measurements in metric units, when possible.
    • Define criteria for “the best” (cleanest, largest, coldest, etc).
    • Repeat the test at least 3 times to see if your results are reproducible.