What are assumptions of normality in parametric tests?

In parametric tests, one of the key assumptions is that the data is normally distributed, which means that the data follows a bell-shaped curve when graphed. This is crucial because many parametric statistical methods, such as t-tests and ANOVA, rely on the property of normality to produce valid results. If the distribution deviates significantly from normality, the results of these tests may be misleading.

Additionally, homogeneity of variances is another vital component of this assumption. This means that the variances across different groups being compared should be approximately equal. When the assumption of homogeneity of variances is violated, it can lead to incorrect conclusions about group differences.

Together, these two aspects—normality of the data distribution and equality of variances—provide the foundational support needed for many statistical analyses, ensuring that the tests conducted are reliable and valid.