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Chi-square test was used when making comparisons between groups.
Individual participant and second-level (random effects) group analyses were conducted making comparisons between groups.
We did the analysis according to the intention to treat principle, making comparisons between groups as randomised.
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The advantage of using high loads of bacteria is that all specimens evolve rather similarly, making comparison between groups and statistical analysis possible.
For parametric data, one-way ANOVA was used to make comparisons between groups followed by Post Hoc (Tukey) test while for non-parametric data, Kruskal Wallis test was used followed by Mann–Whitney U test.
They were also able to make comparisons between groups by both race and income.
Applying ANOVA and Student t-test analysis made comparisons between groups.
We aim to make comparisons between groups and across group types.
Statistical analysis Unless otherwise stated, mean values ± SEM were used to make comparisons between groups.
We reported categorical data as percentages and made comparisons between groups using the χ test.
Post hoc tests (Bonferroni or planned t-tests) were used to make comparisons between groups.
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