The Challenge

Employee engagement scores are falling—but why? Organizations often suspect factors like poor management, lack of growth opportunities, or burnout. Yet without statistical validation, these assumptions remain just that—assumptions. Reactive interventions based on intuition or anecdotes can lead to misdirected efforts, wasted resources, and further disengagement. What’s needed is a way to prove what’s actually driving the decline.

Our Philosophy

Diagnose with data, not guesswork.
We believe meaningful improvements in engagement come from a rigorous understanding of what’s truly affecting your workforce. Hypothesis testing provides that rigor—allowing us to validate or disprove suspected root causes of disengagement with statistical certainty. It’s the difference between assuming and knowing.

The Diagnostic Framework

We apply statistical hypothesis testing—such as t-tests, ANOVA, and Chi-squared tests—to assess engagement patterns and test assumptions.

1. Comparing Groups

• Goal: Identify disparities in survey scores across employee segments.

• Method Use a t-test to compare engagement scores between employees who received leadership training and those who didn’t.

2. Measuring Intervention Impact

• Goal: Evaluate the effect of HR initiatives on engagement.

• Method: Use ANOVA to assess differences in scores across regions piloting different flexible work policies.

3. Testing Categorical Relationships

• Goal: Explore links between categorical variables and engagement.

• Method: Use a Chi-squared test to see if low perceived growth opportunity is significantly associated with low engagement.

4. Identifying Disparities

• Goal: Pinpoint where declines are most acute.

• Method: Analyze whether specific departments, tenures, or demographic groups are disproportionately disengaged.

What This Enables

• Validation of Suspicions: Confirms whether suspected causes (e.g., workload, manager style) actually correlate with lower engagement.

• Identification of Key Drivers: Reveals which variables show statistically significant differences.

• Data-Driven Prioritization: Helps HR focus interventions on areas where the impact is real, not perceived.

• Evidence-Based Decision Making: Moves HR toward analytics-driven credibility.

The Takeaway

Hypothesis testing transforms employee engagement analysis from subjective to scientific. By statistically validating where and why engagement is declining, organizations can:

• Develop targeted interventions tailored to real pain points.

• Optimize resources by focusing only on significant drivers.

• Improve employee experience and retention through focused solutions.

• Elevate HR credibility by bringing analytical precision to people decisions.