Central Limit Theorem: A fundamental concept stating that regardless of the shape of the original population distribution, as long as our sample size is sufficiently large (usually n > 30), its sampling distribution will approximate a normal distribution.
Z-Score: A measure indicating how many standard deviations an observation or value falls above or below the mean in a normal distribution.
Outliers: Data points that significantly deviate from the overall pattern of a dataset, which can affect the assumption of normality.