Module 4
Reduction and Refinement in Scientific Research
Competency: Apply the principles of reduction and refinement to animal testing
Learning Objectives:
- Define the 3Rs Principles of replacement and refinement
- Describe the benefits of reducing the number of animals used in research
- Identify the importance of sample size selection when applying the principle of reduction
- Apply the resource equation to experimental sample size calculations
- Summarize examples of refinement in experimentation
- Describe how refinement can be applied to make experiments more humane in cases where animal use cannot be avoided.
Assessment: Monkey House Project Case Study
- Apply reduction and refinement to an animal-based drug study design
- Calculate an ideal sample size for the case study design using the resource equation
- Identify ways to refine the experimental design to enhance animal welfare
Download Materials
Lesson plan, worksheets, and activities (PDF, 813 KB)
Presentations:
Reduction and Sample Size
(PowerPoint, 20.9 MB)
Refinement and Animal Welfare
(PowerPoint, 26.5 MB)
Linked External Standards:
NGSS
HS-ETS1-1 Analyze a major global challenge to specify qualitative and quantitative criteria and constraints for solutions that account for societal needs and wants.
HS-ETS1-3 Evaluate a solution to a complex real-world problem based on prioritized criteria and trade-offs that account for a range of constraints, including cost, safety, reliability, and aesthetics as well as possible social, cultural, and environmental impacts
CCSS – ELA
RST.11-12.7 Integrate and evaluate multiple sources of information presented in diverse formats and media (e.g., quantitative data, video, multimedia) in order to address a question or solve a problem.
CCSS – Math
HSS.IC.A.1:Understand statistics as a process for making inferences about population parameters based on a random sample from that population.
HSA.CED.A.3: Represent constraints by equations or inequalities, and by systems of equations and/or inequalities, and interpret solutions as viable or nonviable options in a modeling context.