---
title: Run significance testing and read the shading
category: Run Statistical Analysis
slug: run-significance-testing-and-read-shading
blurb: Turn on significance shading in pivot tables, read what the colors mean, and focus on segment differences worth sharing with clients.
order: 1
---

# Run significance testing and read the shading

When you are comparing segments in a banner or crosstab, AddMaple can highlight cells that are higher or lower than expected—so you can spot reliable differences without exporting to another stats package.

This guide covers the workflow: build the table, turn shading on, read the results, and decide what belongs in your deliverable. For how the calculations work (Holm adjustment, Cohen's *h*, weighting), see [Significance Testing](/help/stats/significance-testing).

## When to use significance shading

Use shading when you need to:

- Compare answer distributions across segments (for example region, wave, or buyer type)
- Scan a banner table for lifts or drop-offs worth explaining
- Decide which subgroup differences to include in a deck or Insight Hub

Shading works on **pivot tables with at least two segment columns** and more than one category per axis. For broader driver work across the dataset, start with [Understand what drives outcomes](/help/guides/understand-what-drives-outcomes).

If the stats summary says "no relationship" but individual cells still show shading, see [Why does the stats summary say there's no relationship when details show differences?](/help/frequently-asked-questions/why-does-the-stats-summary-say-no-relationship-when-details-show-differences).

## Step 1: Build the pivot table

1. Pivot the columns you want to compare (for example **Satisfaction** by **Region**)
2. Confirm both axes have more than one category
3. If you use respondent weights, apply them in the pivot builder so counts and shading reflect weighted bases

## Step 2: Turn on significance shading

1. Open the chart menu on the left of the page
2. Toggle **Significance Testing** on
3. AddMaple switches the table to **% of column** view so segments share a comparable base
4. A legend appears under the table explaining the colors

## Step 3: Read the colors

| Visual | Meaning |
|--------|---------|
| No color (white/grey) | Holm-adjusted p > 0.10 — no clear signal |
| Faint circle | Marginal signal (0.05 < p ≤ 0.10) — worth a second look |
| Background shading | p ≤ 0.05 — statistically significant after correction |
| Warmer shading | Observed share **higher** than expected for that cell |
| Cooler shading | Observed share **lower** than expected |
| Deeper color | Stronger effect size (Cohen's *h*) |

Hover a colored cell to see the z-score, Holm-adjusted p-value, effect size, and expected baseline percentage.

Colors stay the same when you switch between **% of column**, **% of row**, and **% of all**—significance is computed from the underlying counts, not the display format.

## Step 4: Focus on segments with enough base

Combine shading with **filters** so you are not over-interpreting thin cells:

1. Filter to the audience or wave you are reporting
2. Check base sizes before calling out a shaded cell in a deliverable
3. Open supporting rows or text comments when a difference needs a "why" story

See [Exploring your data](/help/guides/exploring-data) for filter workflows.

## Step 5: Move findings into your deliverable

Once you trust a difference:

1. Save the chart to **My Collection** or **Add to Dashboard**
2. Add a short callout explaining the segment and direction of the lift
3. For ranked discovery across the whole dataset, use [Related columns](/help/stats/related-columns) alongside table shading

## Related help

- [Significance Testing](/help/stats/significance-testing)
- [Understand what drives outcomes](/help/guides/understand-what-drives-outcomes)
- [Statistical Calculations](/help/frequently-asked-questions/statisticalcalculations)
- [Chi-Square Test](/help/stats/chi-square)