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Differences between different experimental groups were evaluated by ANOVA analysis with Bonferroni as post hoc tests.
Unpaired Student's t test was used to assess differences between different experimental groups at single time points.
As β-actin showed both no differences between different experimental groups and the lowest variation in all samples, it was chosen as reference gene for further analysis.
Statistical tests of one way analysis of variance (ANOVA) followed by the non-parametric test of Kruskal Wallis were used to determine significant differences between different experimental groups and the controls by using GraphPad Instat 3 (GraphPad, San Diego, USA).
**The same superscript letter means no statistically significant difference (P > 0.05) between different experimental groups, whereas different superscript letters mean statistically significant difference among different experimental groups from the same measurement (P < 0.05).
Often the first stages of biomarker screening involves selecting the genes showing the largest and/or most significant fold changes in expression between different experimental groups, and studying the differences in global expression profiles using multifactorial analysis methods such as Principal Component Analysis (PCA) and ANOVA.
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Student's t-test and the Mann-Whitney test were carried out to assay significant differences between the different experimental groups.
Student's t-test, and one-way and two-way ANOVA tests were carried out to assay significant differences between the different experimental groups.
Particularly in experimental designs modulating the load level, analyzing only correct trials that might lead to statistical effects: in the current work, e.g., the number of correct trials decreased with increasing load but this load-related decrease differed between the different experimental groups.
No significant difference of animal mortality was observed between the different experimental groups (p = 0.51 0.54, Fisher exact test).
A two way analysis of variance test (ANOVA) was used for analysis of differences in mean values within and between the different experimental groups and the controls.
More suggestions(15)
between different experimental manipulations
between different experimental days
between different experimental models
between different experimental conditions
between different experimental needs
between different experimental parameters
between different experimental datasets
between different experimental procedures
between different experimental variables
between different experimental setups
between different experimental designs
between different experimental methods
between different experimental sets
between different cultural groups
between different racial groups
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