---
title: "Advanced Statistical Tests"
slug: "advanced-graph-driven-tests"
updated: 2025-03-13T12:19:06Z
published: 2025-03-13T12:19:06Z
canonical: "userguide.dataclassroom.com/advanced-graph-driven-tests"
---

> ## Documentation Index
> Fetch the complete documentation index at: https://userguide.dataclassroom.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Advanced Statistical Tests

Some of the advanced features under the [**Graph Driven Test**](/v1/docs/graph-driven-tests) (GDT) or [**Hypothesis Test**](/v1/docs/hypothesis-tests) functions are only available for users with a College license, giving them the **DataClassroom U** feature set.

[Read more about how to get the College features here.](/v1/docs/college-student-accounts)

Once in DataClassroom U mode, the advanced features will be shown in the test selection window, and the advanced-level tests will be included in the list:

![](https://cdn.document360.io/62f8f41e-e041-42c4-b9bc-8ae1eb45d064/Images/Documentation/image-1631276351217.png)

### Non-parametric tests

Non-parametric tests are the ones to choose if you know your data are not normally distributed. You can choose:

- The Mann-Whitney U test (also known as Wilcoxon rank-sum test) which is a non-parametric version of the T-test
- The Kruskal-Wallis test which is a non-parametric alternative to ANOVA

### Multiple Linear Regression

Multiple Linear Regression allows you to simultaneously look at relationships between a single Dependent Variable and multiple Independent variables.

### Paired T-Test

This tests for a difference in the average value of a numeric variable between two different groups, which are paired by an identifier (name, ID, etc.). For example when a measurement has been made before and after a treatment for each member of a set of individuals.

### Repeated Measures ANOVA

This is similar to the Paired T-Test except more than two samples have been taken per identifier (e.g. a measurement has been taken 3 times for each individual).

### ANCOVA

ANCOVA (ANalysis of COVAriance) tests for the effect of an independent categorical variable (IV) on a numeric dependent variable (DV) while controlling for the effect of a third variable (numeric covariate).

### Running tests in R

The "Try it in R" button takes you to the "Bridge to R" window. [Read more about that here](/v1/docs/bridge-to-r).