Table 1

Glossary A: central feature of the Experimental Design Assistant (EDA) was the development of an ontology, a standardised language to communicate experiments

TermDefinition
BiasThe overestimation or underestimation of the true effect of an intervention. Bias is caused by inadequacies in the design, conduct or analysis of an experiment, resulting in the introduction of error
Biological unit*The entity (eg, mouse, cell line) that we would like to draw a conclusion about
Confounder*A confounder is a nuisance variable that is distributed non-randomly with respect to the independent (treatment) or outcome measure and subsequently can mask an actual association or falsely demonstrate an apparent association
Covariate*A covariate is a continuous variable that is measurable and considered to have a statistical relationship with the outcome measure
Effect sizeQuantitative measure of differences between groups, or strength of relationships between variables
Experimental unitBiological entity subjected to an intervention independently of all other units, such that it is possible to assign any two experimental units to different treatment groups. Sometimes known as unit of randomisation
External validityExtent to which the results of a given study enable application or generalisation to other studies, study conditions, animal strains/species or humans
False negativeStatistically non-significant result obtained when the alternative hypothesis is true. In statistics, it is known as the type II error
False positiveStatistically significant result obtained when the null hypothesis is true. In statistics, it is known as the type I error
Independent variableVariable that the researcher either manipulates (treatment, condition, time), or is a property of the sample (sex) or a technical feature (batch, cage, sample collection) that can potentially affect the outcome measure. Independent variables can be scientifically interesting or can be nuisance variables. Also known as predictor variable
Inference space*Inference space is the population from which the samples in an experiment were drawn and the population to which results of an experiment can be applied
Internal validityExtent to which the results of a given study can be attributed to the effects of the experimental intervention, rather than some other, unknown factor(s) (eg, inadequacies in the design, conduct, or analysis of the study introducing bias)
Nuisance variableVariables that are not of primary interest but should be considered in the experimental design or the analysis because they may affect the outcome measure and add variability. They become confounders if, in addition, they are correlated with an independent variable of interest, as this introduces bias. Nuisance variables should be considered in the design of the experiment (to prevent them from becoming confounders) and in the analysis (to account for the variability and sometimes to reduce bias). For example, nuisance variables can be used as blocking factors or covariates
Observation unit*The entity on which measurements are made
Outcome measureAny variable recorded during a study to assess the effects of a treatment or experimental intervention. Also known as dependent variable, response variable
PowerFor a predefined, biologically meaningful effect size, the probability that the statistical test will detect the effect if it exists (ie, the null hypothesis is rejected correctly)
Sample size (n)Number of experimental units per group, also referred to as n
NTotal number of animals used within an experiment
  • We have therefore used the EDA terminology and definitions78 for consistency. Terms with the * were not defined by the EDA-associated literature.