Research Compliance and Tech Transfer

Guidance for Justifying Animal Numbers in IACUC Protocols

Number: IACUC-GUID-011 

Responsible Office: Office of Research and Creative Scholarship (ORCS) 

Applies to: Principal Investigators Conducting Animal Research  

1. Purpose 

Federal regulations and institutional policy require investigators to provide a clear justification for the number of animals requested in an Institutional Animal Care and Use Committee (IACUC) protocol. This guidance document outlines how investigators should determine and justify animal numbers when submitting new protocols, amendments, or renewals.  

Providing a clear justification helps ensure that animals are used responsibly and ethically, consistent with the principle of Reduction, one of the Three Rs (Replacement, Reduction, Refinement). 

2. Why Justification for Animal Numbers is Required 

Under the Animal Welfare Act and the Public Health Service Policy on Humane Care and Use of Laboratory Animals, the IACUC must ensure that the number of animals requested in a protocol is appropriate to achieve the scientific objectives of the project. The Guide for the Care and Use of Laboratory Animals states that animal numbers should be: 

• Statistically justified whenever possible 
• Limited to the minimum number required to obtain scientifically valid results 
• Consistent with the principle of Reduction 

Animal numbers must be based on scientific need, not on factors such as: 

• How many experiments can be completed in a given time period 
• Laboratory scheduling constraints 
• Cost considerations 

Protocols must also stand alone, meaning reviewers should be able to understand the justification without referencing previous or related protocols.  

Justifications should be written in plain, non-technical language so that they are understandable to: 

• IACUC members 
• Institutional administrators 
• Members of the public who may review animal research documentation 

3. When Statistical Justification May Not Be Appropriate 

While statistical justification is preferred, some types of protocols cannot reasonably use formal statistical power calculations. In these cases, investigators should explain why statistical justification is not appropriate and provide an alternative rationale for the number of animals requested. 

Examples include the following: 

4. When Statistical Justification is Appropriate

The Guide states that statistical analysis should be employed whenever possible to justify that the proposed animal numbers represent the smallest number needed to attain the scientific goals of the project. The information required for a PI to statistically justify the number of animals for a project in order to secure IACUC approval depends on the nature of the statistical evaluation to be used. The box below gives an example of the information required for one type of study and analysis: comparing group means using a Student’s T-test or related parametric analysis. A statistical power analysis will utilize this information to justify the number of animals needed. Other experimental designs or analysis plans (comparisons of proportions, linear regressions, analyses or categorical data, time to event data) can also be statistically justified, but the specific methods required will vary.

For example, the information required for IACUC to evaluate experiments to be evaluated by parametric analyses of continuous dependent variable data includes:

• The P-value used to detect a statistically significant result when one doesn’t exist (false positive) in the study. This value is often denoted by alpha (a) and indicates the probability of finding a similar or larger difference between groups by chance alone. Typical P-values accepted are 0.05 or 0.01; if it is necessary to select an alpha value outside that range, please explain the reason for this to the
• The variability in the response variable expected between animals, frequently expressed as the standard deviation, with literature cited from which PI’s draw these conclusions.
• The minimal effect size the PI wants to detect, again, based on scientific literature and previous studies.
• The statistical power (1- b) desired to correctly reject a false null hypothesis, i.e., the ability to detect an effect if one actually exists. Here, b is the probability of failing to detect a real effect. Statistical power is only of interest when the experimental results support the null hypothesis. For example, the commonly used statistical power of 0.8 (equivalent to a b or Type II error value of 0.2) means that the researcher can be 80% confident that a larger sample size would still not enable rejection of the null hypothesis. If it is necessary to specify a very high statistical power (>0.9), please explain the reason for this to the For example, when studying an endangered species for which the costs of failing to detect real effects outweigh the risks of failing to reject the null hypothesis of no effect (i.e., alpha).

5. Important Considerations for Statistical Justification

6. Tools and Resources for Statistical Justification

Investigators can use a variety of tools to perform sample size calculations.

Examples include:

• Statistical software packages
• Online power analysis tools

For complex experimental designs, investigators are encouraged to consult with a statistician.

7. Assistance and Additional Guidance

Investigators are encouraged to contact the Animal Welfare Program or IACUC Office for assistance in developing appropriate animal number justifications.

Early consultation can help ensure that protocols:

• Meet regulatory requirements
• Use animals responsibly

8. References

1. Guide for the Care and Use of Laboratory Animals (NRC, 2010).
2. Animal Welfare Regulations, 9 CFR, Chapter I, Subchapter A.
3. Public Health Service Policy on Humane Care and Use of Laboratory Animals

9. Review, Approval and Version History