Non-Parametric Tests

Flashcards covering key concepts related to non-parametric tests, normal distribution, and their assumptions.

Non-Parametric Tests

Statistical tests that do not assume a specific distribution for the data, making fewer assumptions than parametric tests.

Normal Distribution

A probability distribution that is symmetric about the mean, where most observations cluster around the central peak and probabilities for values further away from the mean taper off equally in both directions.

Shapiro-Wilk Test

A statistical test that assesses the normality of the data. A p-value < 0.05 indicates that the data is not normally distributed.

Q-Q Plot

A graphical tool used to determine if a dataset follows a specific theoretical distribution by plotting observed data quantiles against theoretical quantiles.

Homoscedasticity

An assumption that the variance of error terms in a statistical model is constant across all levels of the independent variable(s).

Sphericity

The assumption in repeated-measures ANOVA that the variances of differences between all combinations of related groups are equal.

Transformation of Data

A method used to normalize data that is skewed, allowing it to meet the assumptions required for parametric tests.

Parametric Model

A statistical model that assumes that the underlying data follows a certain distribution defined by a finite number of parameters.

Transformation

A process of applying a mathematical operation to data to stabilize variance or normalize distribution.

Levene's Test

A statistical test used to assess the equality of variances for a variable calculated for two or more groups.