Which assumption must parametric tests meet?

Parametric tests are statistical methods that make certain assumptions about the data being analyzed. Each of the listed assumptions is essential for the validity of the results obtained through these tests.

Firstly, normal distribution is a key assumption for parametric tests. This means that the data should be roughly normally distributed in the population from which the samples are drawn. Many parametric tests, such as t-tests and ANOVAs, rely on this assumption as they derive their results based on the properties of the normal distribution.

Secondly, homogeneous variance, also known as homoscedasticity, refers to the assumption that the variances among different groups being compared are approximately equal. This assumption is particularly important in tests involving multiple groups, as it ensures that the results are not skewed by differences in variability.

Lastly, independent observations pertain to the assumption that the scores or measurements are independent of each other. This means that the data points collected for one observation do not influence or relate to another. Independence is vital to ensure that the statistical analysis reflects the true relationship or differences between groups without bias.

Together, these three assumptions—normal distribution, homogeneous variance, and independent observations—form the foundation upon which parametric tests are built. Therefore, acknowledging the necessity of all these factors