Hypothesis tests are based on assumptions about the underlying data. Describing how assumptions were assessed, and whether these assumptions are met by the data, enables readers to assess the suitability of the statistical approach used. If the assumptions are incorrect, the conclusions may not be valid.

The statistical analysis methods implemented will reflect the goals and the design of the experiment, they should be decided in advance before data are collected (see item 19 – Protocol registration). Both exploratory and hypothesis-testing studies might use descriptive statistics to summarise the data (e.g.

In a hypothesis-testing experiment, the primary outcome measure answers the main biological question. It is the outcome of greatest importance, identified in the planning stages of the experiment and used as the basis for the sample size calculation (see item 2 - Sample size).

An outcome measure (also known as a dependent variable or a response variable) is any variable recorded during a study (e.g. volume of damaged tissue, number of dead cells, specific molecular marker) to assess the effects of a treatment or experimental intervention. Outcome measures may be important for characterising a sample (e.g. baseline data) or for describing complex responses (e.g.

Researchers often expect a particular outcome, and can unintentionally influence the experiment or interpret the data in such a way as to support their preferred hypothesis [1]. Blinding (also known as masking) is a strategy used to minimise these subjective biases.