ARRIVE Essential - Results

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​​DISCLAIMER: Information on this and related pages is based on or copied directly from the ARRIVE guidelines 2019 (please see the original guidelines for more information, references and examples that are not included on these pages):

ARRIVE Essential 10 - Item 10 - Results

For each experiment conducted, including independent replications, report:

10a. Summary/descriptive statistics for each experimental group, with a measure of variability where applicable.

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.

Report data for all experiments conducted. If a complete experiment is repeated on a different day, or under different conditions, report the results of all repeats, rather than selecting data from representative experiments. Report the exact number of experimental units per group so readers can gauge reliability of results (see item 2 – Sample size, and item 3 – Inclusion and exclusion criteria). Present data clearly as text, in tables, or in graphs, to enable information to be evaluated, or extracted for future meta-analyses. Report descriptive statistics with a clearly identified measure of variability for each group.

10b. If applicable, the effect size with a confidence interval.

An effect size is a quantitative measure that estimates the magnitude of differences between groups, or relationships between variables. It can be used to assess the patterns in the data collected and make inferences about the wider population from which the sample came. The confidence interval for the effect indicates how precisely the effect has been estimated, and tells the reader about the strength of the effect. For example, if zero is included in the 95% confidence interval, the presence of an effect cannot be assumed. In studies where statistical power is low, and/or hypothesis-testing is inappropriate, providing the effect size and confidence interval indicates how small or large an effect might really be, so a reader can judge the biological significance of the data. Reporting effect sizes with confidence intervals also facilitates extraction of useful data for systematic review and meta-analysis. Where multiple independent studies included in a meta-analysis show quantitatively similar effects, even if each is statistically non-significant, this provides powerful evidence that a relationship is ‘real’, although small.

Report all analyses performed, even those providing non-statistically significant results. Report the effect size, to indicate the size of the difference between groups in the study, with a confidence interval, to indicate the precision of the effect size estimate.

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