Statistics 21

Data are presented as mean with SEM. Statistical analyses comparing treatment groups were performed using a one-way analysis of variance (ANOVA) with Bonferroni’s correction for multiple pairwise comparisons using IBM Statistics 21 (Armonk, NY, formerly SPSS), with P < 0.05 considered significant.

Statistical analyses were performed using Statistica 12PL, Tulusa, USA and IBM Statistics 21, New York, NY, USA. A p-value below 0.05 was adopted as statistically significant. Variable distributions were evaluated with Shapiro–Wilk test, and descriptive statistics (means and standard deviations as well as medians and interquartile ranges) were calculated. The mothers’ anthropometric data and body composition, and the nutritional value of their diet in the first and sixth months of lactation were compared using a paired 2-sample Student’s t-test (normal distribution of differences between all pairs), or a Wilcoxon signed-rank test for paired samples (nonnormally distributed differences between all pairs). A trend analysis of milk composition at three time points was performed with the Jonckheere–Terpstra test, and its effect size was estimated with Kendall’s tau-b correlation coefficient. Correlations between milk composition and the mothers’ body composition and diet were estimated with Pearson’s r correlation coefficient.

Statistical analyses were performed using Statistica 12PL, Tulusa, USA and IBM Statistics 21, New York, NY, USA. A p-value below 0.05 was adopted as statistically significant. Variables distributions were evaluated with a Shapiro–Wilk test and descriptive statistics. Data were presented as means and standard deviations as well as medians and interquartile ranges. Correlations between the intake of fatty acids and fatty acids concentrations in human milk were estimated with Pearson’s r correlation coefficient. Correlations between omega-3 fatty acids (DHA, EPA, ALA) concentrations in human milk, and food consumption frequency were estimated with Kendall’s tau correlation coefficients.

Data were analysed using one-way of variance, and treatment mean separations were performed using Duncan’s multiple range tests at the 5% level of significance in IBM Statistics 21.

Results were analyzed independently for each test. To find the difference between alprazolam and placebo group, we checked normality assumption and employed statistical tests that are appropriate. For each measure, performance under the drug condition was compared with that at baseline using a 2 (time; baseline, after two weeks) × 2 (treatment; placebo, alprazolam) mixed model ANOVA with repeated measures by using IBM Statistics 21 to find the effect of alprazolam over the period of two weeks. Chi-square test was performed for demographic data between groups. p < 0.05 was considered statistically significant.