Essential 10

4. Randomisation

Using appropriate randomisation methods during the allocation to groups ensures that each experimental unit has an equal probability of receiving a particular treatment and provides balanced numbers in each treatment group. Selecting an animal ‘at random’ (i.e. haphazardly or arbitrarily) from a cage is not statistically random as the process involves human judgement. It can introduce bias that influences the results, as a researcher may (consciously or subconsciously) make judgements in allocating an animal to a particular group, or because of unknown and uncontrolled differences in the experimental conditions or animals in different groups. Using a validated method of randomisation helps minimise selection bias and reduce systematic differences in the characteristics of animals allocated to different groups [1-3]. Inferential statistics based on non-randomised group allocation are not valid [4,5]. Thus, the use of randomisation is a prerequisite for any experiment designed to test a hypothesis. Examples of appropriate randomisation methods include online random number generators (e.g., or a function like Rand() in spreadsheet software such as Excel, Google Sheets, or LibreOffice. The EDA has a dedicated feature for randomisation and allocation concealment [6].

Systematic reviews have shown that animal experiments that do not report randomisation or other bias-reducing measures such as blinding, are more likely to report exaggerated effects that meet conventional measures of statistical significance [7-9]. It is especially important to use randomisation in situations where it is not possible to blind all or parts of the experiment but even with randomisation, researcher bias can pervert the allocation. This can be avoided by using allocation concealment (see item 5 – Blinding). In studies where sample sizes are small, simple randomisation may result in unbalanced groups; here randomisation strategies to balance groups such as randomising in matched pairs [10-12] and blocking are encouraged [13]. Reporting the precise method used to allocate animals or experimental units to groups enables readers to assess the reliability of the results and identify potential limitations.

Report the type of randomisation used (simple, stratified, randomised complete blocks, etc.; see “Considerations for the randomisation strategy” below), the method used to generate the randomisation sequence (e.g. computer-generated randomisation sequence, with details of the algorithm or programme used), and what was randomised (e.g. treatment to experimental unit, order of treatment for each animal). If this varies between experiments, report this information specifically for each experiment. If randomisation was not the method used to allocate experimental units to groups state this explicitly and explain how the groups being compared were formed. 


Considerations for the randomisation strategy

Simple randomisation 

All animals/samples are simultaneously randomised to the treatment groups without considering any other variable. This strategy is rarely appropriate as it cannot ensure that comparison groups are balanced for other variables that might influence the result of an experiment.

    Randomisation within blocks  

    Blocking is a method of controlling natural variation among experimental units. This splits up the experiment into smaller sub-experiments (blocks), and treatments are randomised to experimental units within each block [5,13,14]. This takes into account nuisance variables that could potentially bias the results (e.g. cage location, day or week of procedure).

      Stratified randomisation uses the same principle as randomisation within blocks, only the strata tend to be traits of the animal that are likely to be associated with the response (e.g. weight class or tumour size class). This can lead to differences in the practical implementation of stratified randomisation as compared to block randomisation (e.g. there may not be equal numbers of experimental units in each weight class).

        Other randomisation strategies 

        Minimisation is an alternative strategy to allocate animals/samples to treatment group to balance variables that might influence the result of an experiment. With minimisation the treatment allocated to the next animal/sample depends on the characteristics of those animals/samples already assigned. The aim is that each allocation should minimise the imbalance across multiple factors [15]. This approach works well for a continuous nuisance variable such as body weight or starting tumour volume.

          Examples of nuisance variables that can be accounted for in the randomisation strategy 

          • Time or day of the experiment  
          • Litter, cage or fish tank 
          • Investigator or surgeon – different level of experience in the people administering the treatments, performing the surgeries, or assessing the results may result in varying stress levels in the animals or duration of anaesthesia 
          • Equipment (e.g. PCR machine, spectrophotometer) – calibration may vary 
          • Measurement of a study parameter (e.g. initial tumour volume) 
          • Animal characteristics (e.g. sex, age bracket, weight bracket) 
          • Location – exposure to light, ventilation and disturbances may vary in cages located at different height or on different racks, which may affect important physiological processes

          Implication for the analysis  

          If blocking factors are used in the randomisation, they should also be included in the analysis. Nuisance variables increase variability in the sample, which reduces statistical power. Including a nuisance variable as a blocking factor in the analysis accounts for that variability and can increase the power, thus increasing the ability to detect a real effect with fewer experimental units. However, blocking uses up degrees of freedom and thus reduces the power if the nuisance variable does not have a substantial impact on variability. 



          1. Schulz KF, Chalmers I, Hayes RJ and Altman DG (1995). Empirical evidence of bias. Dimensions of methodological quality associated with estimates of treatment effects in controlled trials. Jama. doi: 10.1001/jama.1995.03520290060030
          2. Schulz KF and Grimes DA (2002). Allocation concealment in randomised trials: defending against deciphering. Lancet (London, England). doi: 10.1016/s0140-6736(02)07750-4
          3. Chalmers TC, Celano P, Sacks HS and Smith Jr H (1983). Bias in treatment assignment in controlled clinical trials. New England Journal of Medicine. doi: 10.1056/NEJM198312013092204
          4. Greenberg BG (1951). Why randomize? Biometrics. doi: 10.2307/3001653
          5. Altman DG and Bland JM (1999). Statistics notes. Treatment allocation in controlled trials: why randomise? BMJ. doi: 10.1136/bmj.318.7192.1209
          6. Percie du Sert N, Bamsey I, Bate ST, Berdoy M, Clark RA, Cuthill I, Fry D, Karp NA, Macleod M, Moon L, Stanford SC and Lings B (2017). The Experimental Design Assistant. PLoS Biol. doi: 10.1371/journal.pbio.2003779
          7. Hirst JA, Howick J, Aronson JK, Roberts N, Perera R, Koshiaris C and Heneghan C (2014). The need for randomization in animal trials: an overview of systematic reviews. PLoS ONE. doi: 10.1371/journal.pone.0098856
          8. Vesterinen HM, Sena ES, ffrench-Constant C, Williams A, Chandran S and Macleod MR (2010). Improving the translational hit of experimental treatments in multiple sclerosis. Multiple Sclerosis Journal. doi: 10.1177/1352458510379612
          9. Bebarta V, Luyten D and Heard K (2003). Emergency medicine animal research: does use of randomization and blinding affect the results? Acad Emerg Med. doi: 10.1111/j.1553-2712.2003.tb00056.x
          10. Taves DR (1974). Minimization: a new method of assigning patients to treatment and control groups. Clinical pharmacology and therapeutics. doi: 10.1002/cpt1974155443
          11. Saint-Mont U (2015). Randomization does not help much, comparability does. PLOS ONE. doi: 10.1371/journal.pone.0132102
          12. Laajala TD, Jumppanen M, Huhtaniemi R, Fey V, Kaur A, Knuuttila M, Aho E, Oksala R, Westermarck J, Mäkelä S, Poutanen M and Aittokallio T (2016). Optimized design and analysis of preclinical intervention studies in vivo. Scientific reports. doi: 10.1038/srep30723
          13. Bate ST and Clark RA (2014). The design and statistical analysis of animal experiments. Cambridge University Press.
          14. Kang M, Ragan BG and Park JH (2008). Issues in outcomes research: an overview of randomization techniques for clinical trials. J Athl Train. doi: 10.4085/1062-6050-43.2.215
          15. Altman DG and Bland JM (2005). Treatment allocation by minimisation. BMJ. doi: 10.1136/bmj.330.7495.843

          Example 1 

          “Fifty 12-week-old male Sprague-Dawley rats, weighing 320–360g, were obtained from Guangdong Medical Laboratory Animal Center (Guangzhou, China) and randomly divided into two groups (25 rats/group): the intact group and the castration group. Random numbers were generated using the standard = RAND() function in Microsoft Excel.” [1]    

          Example 2 

          “Animals were randomized after surviving the initial I/R, using a computer based random order generator.” [2] 

          Example 3 

          “At each institute, phenotyping data from both sexes is collected at regular intervals on age-matched wildtype mice of equivalent genetic backgrounds. Cohorts of at least seven homozygote mice of each sex per pipeline were generated…The random allocation of mice to experimental group (wildtype versus knockout) was driven by Mendelian Inheritance.” [3] 



          1. Zhao S, Kang R, Deng T, Luo L, Wang J, Li E, Luo J, Liu L, Wan S and Zhao Z (2018). Comparison of two cannulation methods for assessment of intracavernosal pressure in a rat model. PLoS One. doi: 10.1371/journal.pone.0193543
          2. Jansen of Lorkeers SJ, Gho JMIH, Koudstaal S, van Hout GPJ, Zwetsloot PPM, van Oorschot JWM, van Eeuwijk ECM, Leiner T, Hoefer IE, Goumans M-J, Doevendans PA, Sluijter JPG and Chamuleau SAJ (2015). Xenotransplantation of Human Cardiomyocyte Progenitor Cells Does Not Improve Cardiac Function in a Porcine Model of Chronic Ischemic Heart Failure. Results from a Randomized, Blinded, Placebo Controlled Trial. PLOS ONE. doi: 10.1371/journal.pone.0143953
          3. Karp NA, Mason J, Beaudet AL, Benjamini Y, Bower L, Braun RE, Brown SDM, Chesler EJ, Dickinson ME, Flenniken AM, Fuchs H, Angelis MHd, Gao X, Guo S, Greenaway S, Heller R, Herault Y, Justice MJ, Kurbatova N, Lelliott CJ, Lloyd KCK, Mallon A-M, Mank JE, Masuya H, McKerlie C, Meehan TF, Mott RF, Murray SA, Parkinson H, Ramirez-Solis R, et al. (2017). Prevalence of sexual dimorphism in mammalian phenotypic traits. Nature communications. doi: 10.1038/ncomms15475



          Ensuring there is no systematic difference between animals in different groups apart from the experimental exposure is an important principle throughout the conduct of the experiment. Identifying nuisance variables (sources of variability or conditions that could potentially bias results) and managing them in the design and analysis increases the sensitivity of the experiment. For example, rodents in cages at the top of the rack may be exposed to higher light levels, which can affect stress [1].

          Reporting the strategies implemented to minimise potential differences that arise between treatment groups during the course of the experiment, enables others to assess the internal validity. Strategies to report include: standardising (keeping conditions the same, e.g. all surgeries done by the same surgeon), randomising (e.g. the sampling or measurement order), blocking or counterbalancing (e.g. position of animal cages or tanks on the rack), to ensure groups are similarly affected by a source of variability. In some cases, practical constraints prevent some nuisance variables from being randomised, but they can still be accounted for in the analysis (see item 7 – Statistical methods).

          Report the methods used to minimise confounding factors alongside the methods used to allocate animals to groups. If no measures were used to minimise confounders (e.g. treatment order, measurement order, cage or tank position on a rack), explicitly state this and explain why.



          1. Ishida A, Mutoh T, Ueyama T, Bando H, Masubuchi S, Nakahara D, Tsujimoto G and Okamura H (2005). Light activates the adrenal gland: timing of gene expression and glucocorticoid release. Cell Metab. doi:10.1016/j.cmet.2005.09.009

          Example 1 

          “Randomisation was carried out as follows. On arrival from El-Nile Company, animals were assigned a group designation and weighed. A total number of 32 animals were divided into four different weight groups (eight animals per group). Each animal was assigned a temporary random number within the weight range group. On the basis of their position on the rack, cages were given a numerical designation. For each group, a cage was selected randomly from the pool of all cages. Two animals were removed from each weight range group and given their permanent numerical designation in the cages. Then, the cages were randomized within the exposure group.” [1] 

          Example 2 

          “...test time was between 08.30am to 12.30pm and testing order was randomized daily, with each animal tested at a different time each test day.” [2] 

          Example 3 

          “Bulls were blocked by BW into four blocks of 905 animals with similar BW and then within each block, bulls were randomly assigned to one of four experimental treatments in a completely randomized block design resulting in 905 animals per treatment. Animals were allocated to 20 pens (181 animals per pen and five pens per treatment).” [3] 



          1. El-Agroudy NN, El-Naga RN, El-Razeq RA and El-Demerdash E (2016). Forskolin, a hedgehog signalling inhibitor, attenuates carbon tetrachloride-induced liver fibrosis in rats. Br J Pharmacol. doi: 10.1111/bph.13611
          2. Carrillo M, Migliorati F, Bruls R, Han Y, Heinemans M, Pruis I, Gazzola V and Keysers C (2015). Repeated Witnessing of Conspecifics in Pain: Effects on Emotional Contagion. PLOS ONE. doi: 10.1371/journal.pone.0136979
          3. Del Bianco Benedeti P, Paulino PVR, Marcondes MI, Maciel IFS, da Silva MC and Faciola AP (2016). Partial Replacement of Ground Corn with Glycerol in Beef Cattle Diets: Intake, Digestibility, Performance, and Carcass Characteristics. PLOS ONE. doi: 10.1371/journal.pone.0148224