Volume 179, Issue 15 p. 3914-3914
DECLARATION
Free Access

Declaration of transparency and scientific rigour: Checklist for Design and Analysis 2022

First published: 07 June 2022
Citations: 1

This checklist for design and analysis provides guidance for transparent reporting and scientific rigour of preclinical research as set out in Experimental design and analysis and their reporting II: Updated and simplified guidance for authors and peer reviewers (Curtis et al., 2022). This checklist is intended as a guide for submission to the British Journal of Pharmacology.

Criteria Number Issue Where to place information
Experimental design 1a Details of prior sample size estimation (e.g., power calculations) should be provided, and, if it was not conducted, a valid scientific justification is provided. Methods
1b The methods declare whether or not randomization was undertaken. If it was not conducted, a valid scientific justification is provided. Methods
1c The methods declare whether blinding was undertaken, and, if not, a valid scientific justification is provided. Methods
Group sizes 2a Group size (n) refers to biological samples and not technical replicates. Methods
2b Inferential statistics (comparisons between groups) are undertaken only if n = 5/group or more. A valid explanation is provided for data with n of less than 5. Methods/Results
2c The exact group sizes (n) are provided, not a range. The N number of replicates where relevant should be provided as well as the n value for independent experiments. Methods/Results within figure/table legends
Statistical plan 3a A data and statistical analysis section is provided giving details of all summary and inferential statistical tests used. Methods
3b Details are provided of any statistical package or programme employed, and details of which tests used in the Data and Statistical Analysis section. Methods
Data and statistical analysis 4a If ANOVA is used, a statement is provided indicating that post hoc tests were conducted only if the data are normally distributed, and there is no inhomogeneity of variance. Methods
4b

The presentation and processing of a data set should map to its mathematical distribution.

Results
4c Any data normalization is explained with a valid scientific justification (i.e., to control for unwanted sources of variation). If normalization generates values with no variance (i.e., control SEM = 0), the data should not be subjected to parametric statistical analysis. Results
4d ANCOVA is a useful approach that accounts for sources of intervention-unrelated variation and should be used where relevant. Results
Level of probability 5a The threshold P value deemed to constitute statistical significance (α) should be defined in the Methods. Methods/Results
5b Authors may elect to depict statistical significance as a single value in the Results and figures usually denoted as *. Results
5c Authors may elect to report the full value of P, but this approach must be accompanied by a statement in the data and statistical analysis subsection that statistical significance is only stated where P < α. Methods/Results
Outliers/exclusion criteria 6 Inclusion and/or exclusion criteria are clearly defined in the methods. Methods