The interpretation of normality tests, such as the Shapiro-Wilk test or the Kolmogorov-Smirnov test, depends on various factors, and there isn't a universal threshold that applies to all situations. The choice of significance level (commonly set at 0.05) is somewhat arbitrary, and p-values close to this threshold should be interpreted cautiously. In practice, the decision on whether data can be considered approximately normally distributed often depends on the context of the analysis and the assumptions of the statistical methods being used. A p-value slightly above 0.05 might not necessarily imply a departure from normality that would seriously impact the validity of the analysis. It's essential to consider the specific requirements of the statistical methods you are using and the robustness of those methods to deviations from normality. It's worth noting that normality is an assumption for some parametric statistical tests (e.g., t-tests, ANOVA), and violating this as...