## Experimental design/statisticsNC3Rs Links | External Links | Legislation | References | Events Also see our experimental design and statistical analysis programme. Well designed and correctly analysed experiments can lead to a reduction in animal use whilst increasing the scientific validity of the results. The number of animals used should be the minimum number that is consistent with the aims of the experiment. A well designed experiment should be: ## 1. UnbiasedWhen two or more treatment groups are compared, the animals in the groups should be in identical environments and be similar in every way apart from the applied treatments. Bias can be minimised by: - Randomly allocating animals to the treatment groups (a physical process is needed e.g. tossing a coin, picking a number)
- Ensuring that all subsequent treatments (including housing) are applied in a random order
- Ensuring that researchers analysing experimental outcomes are unaware of the treatment received (blinded) until the final statistical analysis.
## 2. Adequately powered (i.e. use sufficient animals)Powerful experiments are ones that have the maximum chance of detecting a true treatment effect. Power is achieved by: - Using appropriate numbers of animals (sample size)
- Controlling inter-subject variation (e.g. using randomisation)
- The type of statistical test to be used (e.g. a t-test or a chi-squared test to compare two proportions)
- The significance level to be used (often a 5% level)
- The required statistical power (usually 80-90%),
- The sidedness of the test (a 2-sided test is usual)
- The effect size of biological interest (i.e. how much of a difference in biological or clinical effect it is necessary to detect)
- An estimate of the standard deviation (when comparing means) this must come from a previous experiment
The StatPages.org website offers online calculations of sample size combining the above factors.
Where N = total number of subjects (e.g. individual animals or groups/cages of animals) and T = the number of treatment combinations, E (the sample size) should be approximately between 10 and 20. For example, an experiment comparing four treatments, using six rats per treatment, would have N = 24 (6 x 4) and T = 4, therefore E = 24 4 = 20. This is within the acceptable range. However, there may be good reasons for going above this upper limit. If E is 30 or 40, the experiment may be too large and may waste resources. This equation is most appropriate for small, non-routine and more complex animal experiments likely to be analysed using the analysis of variance statistical method (ANOVA). ## 3. Have a wide range of applicabilityIt is often useful to find out whether similar results are obtained in males and females, in different strains, or as a result of different diets or environments. Similarly, the response to a drug may depend on prior treatment, the effects of other drugs, or the route of administration. These effects can be studied efficiently using factorial experimental designs.
## 4. Be simple and efficientExperiments should not be so complicated that mistakes are made in their execution, or the statistical analysis becomes unduly complicated. Small pilot studies should be used before starting a major experiment to ensure that the experiment is logistically efficient and to give some preliminary indication of likely results. All experiments should be pre-planned, and should not be changed while they are in progress. ## 5. Indicate the range of certaintyEach experiment should be statistically analysed so that the results can be used in planning future experiments. An appropriate statistical analysis should indicate the range of uncertainty in the results, or the measure of variation, usually indicated by significance levels or confidence intervals. These factors are discussed in more detail by Dell et al. (2002), Festing et al. (2002) and more briefly by Festing and Altman (2002) (see 'References' above). |
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## Resources## Links- Pezzullo JC (2006), StatPages, Comprehensive website on performing statistical calculations
Open Link - NC3Rs (2006), Experimental Design Working Group
Open Link - Russell WMS, Burch RL (1959), The Principles of Humane Experimental Technique
Open Link
## References- Harmonising the definition of refinement.
View PDF (853KB) - Improving bioscience research reporting: the ARRIVE guidelines for reporting animal research.
Open Link - Why do a pilot study?.
View PDF - Guidelines for the design and statistical analysis of experiments using laboratory animals.
View PDF - Sample size determination.
View PDF - Survey of the quality of experimental design, statistical analysis and reporting of research using animals.
Open Link View PDF (347KB) Open Link
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