# Compare groups

Compare Groups examines independent samples and makes inferences about the differences between them.

Independent samples occur when observations are made on different sets of items or subjects. If the values in one sample do not tell you anything about the values in the other sample, then the samples are independent. If the knowing the values in one sample could tell you something about the values in the other sample, then the samples are related.

**Side-by-side univariate plots**

Side-by-side univariate plots summarize the distribution of data stratified into groups.**Equality of means/medians hypothesis test**

An equality hypothesis test formally tests if two or more population means/medians are different.**Equivalence of means hypothesis test**

An equivalence hypothesis test formally tests if two population means are equivalent, that is, practically the same.**Difference between means/medians effect size**

An effect size estimates the magnitude of the difference between two means/medians.**Multiple comparisons**

Multiple comparisons make simultaneous inferences about a set of parameters.**Mean-Mean scatter plot**

A mean-mean scatter plot shows a 2-dimensional representation of the differences between many means.**Homogeneity of variance hypothesis test**

A homogeneity hypothesis test formally tests if the populations have equal variances.

**Available in Analyse-it Editions**

Standard edition

Method Validation edition

Quality Control & Improvement edition

Ultimate edition

- What is Analyse-it?
- What's new?
- Administrator's Guide
- User's Guide
- Statistical Reference Guide
- Distribution
- Compare groups
- Calculating univariate descriptive statistics, by group
- Side-by-side univariate plots
- Creating side-by-side univariate plots
- Equality of means/medians hypothesis test
- Equivalence of means hypothesis test
- Tests for means/medians
- Testing equality of means/medians
- Testing equivalence of means
- Difference between means/medians effect size
- Estimators for the difference in means/medians
- Estimating the difference between means/medians
- Multiple comparisons
- Mean-Mean scatter plot
- Multiple comparison procedures
- Comparing multiple means/medians
- Homogeneity of variance hypothesis test
- Tests for homogeneity of variance
- Testing homogeneity of variance
- Study design
- Compare pairs
- Contingency tables
- Correlation and association
- Principal component analysis (PCA)
- Factor analysis (FA)
- Item reliability
- Fit model
- Method comparison
- Measurement systems analysis (MSA)
- Reference interval
- Diagnostic performance
- Control charts
- Process capability
- Pareto analysis
- Study Designs
- Bibliography

Version 6.00

Published 27-Apr-2022