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We test for differential expression using a Welch's t-test for a difference in means of samples with unequal variances, and use the sum of the negative log P values across the nine cohorts as a single summary statistic per gene.
Because each GPS data point could be considered as a single sample case, and multiple samples with unequal sizes were collected from each person, a random effects regression model is constructed to consider unobserved heterogeneity among drivers [26, 27].
To compare CPT values between controls, headache groups and subcategory of headache patients' features, we used independent unpaired samples (with unequal variances) t test and Bonferroni correction for multiple comparison; all p values were two-sided.
For significance, Microsoft Excel was used to perform Student's t-Tests, assuming 2-tailed distribution of two independent samples with unequal variance.
The median and variances of period distribution were analyzed by T-test for samples with unequal variances and F-test, respectively.
Prior to the appearance of the knife, the skin conductance was not significantly different between the two groups (t-test performed on the means for samples with unequal variance: p = 0.89; 7.2±3.7 µS [mean±standard deviation] for the "observation only group, 6.2±4.7 µS for the "observe and imagine" group).
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A two-phase approach for sampling with unequal inclusions probabilities and fixed sample size is presented.
Survey designs typically specify that individuals are to be sampled with unequal probabilities of selection.
Most cohort studies employ multistage cluster sampling with unequal probabilities to conveniently select participants with desired characteristics, and participants from different clusters might be genetically related.
Statistical significance of the average mRNA/protein expression in the cluster/operon was estimated by p-value in the heteroscedastic t-test (two-samples with unequal variance).
The log2 transformed normalized expression values, from each microarray, were also analyzed in a two tailed t-test (two-sample with unequal variance) in Microsoft Excel.
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