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Two-tailed Student's t-test (parametric) or Wilcoxon rank sum test (non-parametric) were employed for data analysis by GraphPad Prism 5.0 (San Diego, CA).
Two methods of representing the pooled parametric and non-parametric data were employed: a fixed effects model weighted by inverse variance and a random effects model.
Normality was used when appropriate to determine whether parametric or non-parametric tests were employed.
Questionnaire data were initially checked for normality of distribution with the Kolmogorov Smirnov test and were found normally distributed (P > 0.05), therefore parametric statistics were employed.
A nonparametric and a parametric model were employed to estimate WTP values for two electricity products; Grid Electricity (GE) which is largely provided by Government and Photovoltaic (PV) electricity which is provided by both government and other private service providers.
Where data did not violate assumptions of normality parametric statistics were employed; otherwise, nonparametric statistics were used.
The interval measurements were normally distributed, and therefore several parametric tests were employed to analyze data.
Mann-Whitney U tests (nonparametric data) and Student's t-tests (parametric data) were employed for the statistical analyses.
Non parametric analyses were employed to evaluate differences in IP-10 plasma levels between subject groups using Wilcoxon rank sum analyses (α level of 0.05).
Spearman's rho and Mann–Whitney U-tests (MW-U-tests) were chosen for correlations and group comparisons respectively and parametric tests were employed for exploratory analyses only when no comparable nonparametric test was available, such as partial correlation or analysis of covariance (ANCOVA).
Parametric statistics were used in the analyses when our data confirmed to normality (Shapiro and Wilke's test, P>0.05), whereas non-parametric statistics were employed when these requirements were not fulfilled.
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