CPSY102_Overview and Assumptions of Parametric Statistics -3

Parametric Statistics

Statistical methods that involve assumptions about the distribution of the population from which the samples are drawn, often assuming a normal distribution.

Nonparametric Statistics

Statistical methods that do not require any assumptions about the distribution of the population; suitable for nominal or ordinal data.

Normality Assumption

The assumption that the data follows a normal distribution, which is crucial for many statistical tests.

Homogeneity of Variance

The assumption that different samples have the same variance; essential for fair group comparison in ANOVA.

Independence

The assumption that the data collected from one observation does not influence or relate to other observations.

t-test

A parametric test used to compare the means of two groups.

ANOVA (Analysis of Variance)

A parametric test used to compare the means of three or more groups.

Mann-Whitney U Test

A nonparametric test that compares two independent groups when the data is not normally distributed.

Kruskal-Wallis Test

A nonparametric alternative to ANOVA for comparing three or more independent groups.

Shapiro-Wilk Test

A statistical test used to assess whether a sample comes from a normally distributed population.

Levene's Test

A test that checks if the variances across groups are equal, less sensitive to non-normality.

Critical Value

A threshold that determines whether to reject the null hypothesis in statistical testing.

Type I Error

False positive; rejecting the null hypothesis when it is true.

Type II Error

False negative; failing to reject the null hypothesis when it is false.

Peason Coefficient of Correlation

A parametric test that measures the linear relationship between two continuous variables.

Spearman’s Rank Correlation

A nonparametric test that measures the strength of a monotonic relationship between two variables.