Hypothesis Testing (Non-Parametric) · TOOL GUIDE

Friedman Test

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Understand the method. Use it with confidence.

Compare 3+ related groups (Non-parametric Repeated Measures). Quantly runs the method against your active analysis context and returns structured results for review and interpretation.

01

When to use it

Use this tool for rank-, count-, or distribution-based comparisons when parametric assumptions are unsuitable or variables are categorical/ordinal.
Use it only when the selected variables and study design match the method's requirements.

02

How to use it

  1. Open the tool from the Quantly Tools panel.
  2. Confirm the active dataset and analysis session.
  3. Set Subject ID Column (Required): Column that uniquely identifies each subject.
  4. Set Within-Subject Factor (Time/Condition) (Required): Categorical variable representing the repeated conditions.
  5. Set Dependent Variable (Numeric) (Required): The numeric outcome variable measured at each condition.
  6. Set Significance Level (α) (Required): The threshold for statistical significance.
  7. Set Generate Box Plots by Condition (Optional): Set generate box plots by condition.
  8. Set Generate Profile Plot (Optional): Set generate profile plot.
  9. Set Perform Post-Hoc Test (Conover's) (Optional): If the main test is significant, perform pairwise comparisons to see which conditions differ.
  10. Review optional or advanced settings and keep defaults unless your design requires a change.
  11. Run the tool, inspect warnings and diagnostics, then save or export the result with its assumptions.
03

How to interpret results

Interpret the test statistic and p-value with an effect-size measure where available. A significant result indicates evidence of a difference or association, not its practical size or causal direction.