Hypothesis Testing (Parametric) · TOOL GUIDE

Two-Sample Z-Test

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Understand the method. Use it with confidence.

Compare two means (known population SDs). 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 to test a prespecified mean-based hypothesis when observations are appropriately sampled and the model assumptions are reasonable.
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 Numeric Variable (Required): Select the numeric variable to compare.
  4. Set Grouping Variable (Required): Select the categorical variable with exactly two groups.
  5. Set Population SD for Group 1 (σ₁) (Required): Known population standard deviation for the first group. Must be > 0.
  6. Set Population SD for Group 2 (σ₂) (Required): Known population standard deviation for the second group. Must be > 0.
  7. Set Alternative Hypothesis (Required): Specifies the alternative hypothesis.
  8. Set Significance Level (α) (Required): The threshold for statistical significance.
  9. Set Generate Side-by-side Box Plots (Optional): Set generate side-by-side box plots.
  10. Set Generate Distribution Plots (Optional): Set generate distribution plots.
  11. Set Generate Group Means with Confidence Intervals (Optional): Set generate group means with confidence intervals.
  12. Set Generate Overlapping Normal Distributions (Optional): Set generate overlapping normal distributions.
  13. Set Generate Difference in Means with CI Plot (Optional): Set generate difference in means with ci plot.
  14. Review optional or advanced settings and keep defaults unless your design requires a change.
  15. Run the tool, inspect warnings and diagnostics, then save or export the result with its assumptions.
03

How to interpret results

Interpret the p-value against the chosen alpha level, then examine the effect estimate and confidence interval. Statistical significance is not the same as practical importance; verify assumptions and multiplicity.