The normality assumption is a statistical premise that assumes data follows a normal distribution, which is crucial for the validity of many parametric tests such as t-tests and ANOVA. Deviations from normality can lead to incorrect conclusions, making it essential to assess and, if necessary, transform data to meet this assumption.

Normality Assumption

normality assumption

statistical premise

The normal distribution, also known as the Gaussian distribution, is a continuous probability distribution characterized by its symmetrical bell-shaped curve, where the mean, median, and mode are all equal. It is fundamental in statistics because many natural phenomena and measurement errors are approximately normally distributed, making it a cornerstone for statistical inference and hypothesis testing.

normal distribution

Validity of parametric tests

t-tests

ANOVA, or Analysis of Variance, is a statistical method used to determine if there are significant differences between the means of three or more independent groups. It helps in understanding whether the observed variations between group means are due to actual differences or random chance.

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