Bias: The over- or under-estimation 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.
Descriptive and inferential statistics: Descriptive statistics are used to summarise the data. They generally include a measure of central tendency (e.g. mean or median) and a measure of spread (e.g. standard deviation or range). Inferential statistics are used to make generalisations about the population from which the samples are drawn. Hypothesis tests such as ANOVA, Mann-Whitney or t-tests are examples of inferential statistics.
Effect size: Quantitative measure of differences between groups, or strength of relationships between variables.
Experimental unit: Biological 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 validity: Extent to which the results of a given study enable application or generalisation to other studies, study conditions, animal strains/species, or humans.
False negative: Statistically non-significant result obtained when the alternative hypothesis is true. In statistics, it is known as the type II error.
False positive: Statistically significant result obtained when the null hypothesis is true. In statistics, it is known as the type I error.
Independent variable: Variable 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 nuisance variables. Also known as: predictor variable.
Internal validity: Extent 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) (e.g. inadequacies in the design, conduct, or analysis of the study introducing bias).
Nuisance variable: Variables 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.
Null and alternative hypotheses: The null hypothesis (H0) is that there is no effect, such as a difference between groups or an association between variables. The alternative hypothesis (H1) postulates that an effect exists.
Outcome measure: Any variable recorded during a study to assess the effects of a treatment or experimental intervention. Also known as: dependent variable, response variable.
Power: For a predefined, biologically meaningful effect size, the probability that the statistical test will detect the effect if it exists (i.e. the null hypothesis is rejected correctly).
Sample size: Number of experimental units per group, also referred to as n.