Animal Use in Science: Exploring the 3Rs

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)


Reduction and Sample Size
(PowerPoint, 20.9 MB)

Refinement and Animal Welfare
(PowerPoint, 26.5 MB)

Linked External Standards:


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


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.