The normality assumption is the principle that data within a population or a sample follows a normal distribution, which is crucial for various statistical analyses. This assumption underpins many statistical methods, including confidence intervals and hypothesis tests, as it allows for valid inferences about population parameters when the underlying distribution of the data is unknown. When working with small samples, this assumption becomes particularly important because non-normal data can lead to inaccurate results.