How to Avoid False Insights When Analyzing Multiple Groups in HR Data
As practitioners of people analytics, we frequently engage in multiple comparisons across various HR domains—employee engagement, compensation, diversity, and performance. This multiplicity of analyses introduces a significant challenge: the increased probability of Type I errors, or false positives. This is where the Bonferroni correction comes into play. The Bonferroni correction can be used whenever multiple comparisons are made to ensure that your results are robust and avoid false positives.
In HR, it’s common to run multiple tests on different groups. For instance, you might want to know:
Each of these comparisons introduces the risk of making a Type I error—incorrectly concluding that a difference exists when it doesn’t. The more comparisons you make, the more likely it becomes that at least one result will be statistically significant purely by chance, leading to false insights and potentially flawed decision-making.
Normally, we use a significance level of 0.05, meaning there’s a 5% chance of incorrectly finding a difference when there isn’t one. However, if we run 20 tests, there’s a much higher probability of getting at least one false positive. The Bonferroni correction addresses this issue by adjusting the significance threshold to account for the number of comparisons made.
With the Bonferroni correction, the standard significance level (0.05) is divided by the number of comparisons. For example, if you’re making 10 comparisons, the new significance level would be 0.05 ÷ 10 = 0.005. This adjustment reduces the chances of false positives and ensures the results you act on are more likely to reflect true differences.
Engagement Survey Analysis
When dissecting engagement survey results across multiple dimensions (e.g., gender, tenure, department), the Bonferroni correction ensures that observed differences are substantive rather than products of chance.
Pay Equity Studies
In analyzing compensation across various factors like job level, department, and demographics, the correction mitigates the risk of identifying spurious pay gaps, leading to more accurate and actionable insights.
Diversity and Inclusion Metrics
D&I analyses often involve numerous comparisons across demographic groups on metrics such as promotion rates, representation in leadership, and inclusion scores. The Bonferroni correction helps maintain the validity of these analyses.
While the Bonferroni correction is invaluable for controlling familywise error rates, it's important to note its conservative nature. In some cases, it may increase the likelihood of Type II errors (false negatives). Alternative methods like the Holm-Bonferroni or the false discovery rate (FDR) approach might be considered for more nuanced analyses.
The application of the Bonferroni correction in HR and people analytics is not merely a statistical nicety—it's a fundamental component of rigorous analysis. By implementing this method, we enhance the credibility of our insights, ensuring that our data-driven HR strategies are built on a solid statistical foundation.
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