Provide enough detail for the reader to assess the suitability of the animal model used to address the research question. Include information on the rationale for choosing a particular species, explain how the outcome measures assessed are relevant to the condition under study, and how the model was validated. Stating that an animal model is commonly used in the field is not appropriate, and a well-considered, detailed rationale should be provided.

Scientific background information for an animal study should demonstrate a clear evidence gap and explain why an in vivo approach was warranted. Systematic reviews of the animal literature provide the most convincing evidence that a research question has not been conclusively addressed, by showing the extent of current evidence within a field of research. They can also inform the choice of animal model by providing a comprehensive overview of the models used along with their benefits and limitations [1-3].

A transparent and accurate abstract increases the utility and impact of the manuscript, and allows readers to assess the reliability of the study [1]. The abstract is often used as a screening tool by readers to decide whether to read the full article or whether to select an article for inclusion in a systematic review.

In hypothesis-testing studies using inferential statistics, investigators frequently confuse statistical significance and small p-values, with biological or clinical importance [1]. Statistical significance is usually quantified and evaluated against a preassigned threshold, with p < 0.05 often used as a convention. However, statistical significance is heavily influenced by sample size and variation in the data (see item 2 – Sample size).

Summary/descriptive statistics provide a quick and simple description of the data, they communicate quantitative results easily and facilitate visual presentation. For continuous data, these descriptors include a measure of central tendency (e.g. mean, median) and a measure of variability (e.g. quartiles, range, standard deviation) to help readers assess the precision of the data collected. Categorical data can be expressed as counts, frequencies, or proportions.

There may be numerous approaches to investigate any given research problem, therefore it is important to explain why a particular procedure or technique was chosen. This is especially relevant when procedures are novel or specific to a research laboratory, or constrained by the animal model or experimental equipment (e.g. route of administration determined by animal size [1]).