6.4.1 Assumptions Underlying Parametric Tests

Normality: The data must be approximately normally distributed, formally assessed using the Shapiro-Wilk test for sample sizes below approximately 50,...

• Normality: The data must be approximately normally distributed, formally assessed using the Shapiro-Wilk test for sample sizes below approximately 50, or the Kolmogorov-Smirnov test for larger samples.
• Homogeneity of variance: The groups being compared should have approximately equal variance, assessed using Levene's test or the F-test for two groups, or Bartlett's test across multiple groups.
• Independence: Individual observations must be independent of one another, except in explicitly paired or repeated-measures designs.
• Scale of measurement: The outcome variable must be measured on an interval or ratio scale, not an ordinal or nominal scale.
• Violated assumptions: If any of the above assumptions is violated, the appropriate non-parametric equivalent should be used, or the data may be mathematically transformed (commonly by logarithmic or square-root transformation) before re-testing for normality.