As people analytics becomes an essential tool for HR decision-making, the use of statistical methods to draw meaningful insights from employee data is increasingly common. When performing these analyses, two key statistical concepts often arise: p-values and effect sizes. While both are integral to understanding the results of statistical tests, they serve very different purposes—and understanding the distinction between them is critical for HR professionals and people analysts who want to make data-driven decisions that genuinely improve their organizations.

In this article, we’ll explore the roles of p-values and effect sizes, clarify their importance in people analytics, and discuss when and how each should be used to guide strategic HR initiatives.

What is a p-Value?

A p-value is a statistical measure used to determine whether the result of a hypothesis test is statistically significant. In simpler terms, it tells us whether an observed effect, like a difference in engagement scores between two teams, is likely to have occurred by chance.

When running statistical tests like t-tests or regression analyses, we compare the p-value to a significance threshold (commonly set at 0.05). If the p-value is below this threshold, we reject the null hypothesis and conclude that there is a statistically significant effect.

For example: If we’re testing whether a new leadership training program affects employee engagement, and we get a p-value of 0.03, it suggests that there’s only a 3% chance that the difference in engagement scores occurred by random chance. This is statistically significant because it falls below the 0.05 threshold.

However, while p-values tell us whether an effect is likely to be real, they don’t tell us how strong or important that effect is. This is where effect sizes come into play.

What is an Effect Size?

An effect size measures the magnitude of a relationship or difference between variables. In people analytics, effect sizes give us a sense of how impactful a particular factor is. For example, in an analysis of the impact of job satisfaction on turnover, the effect size would tell us how much job satisfaction influences turnover rates.

Effect sizes provide valuable context that p-values alone cannot. A statistically significant p-value may indicate that a relationship exists, but without knowing the effect size, you won’t know whether that relationship is meaningful in practical terms.

Common measures of effect size:

• Cohen’s d: Used to measure the difference between two groups (e.g., male vs. female employees on job satisfaction).
• Pearson’s r: Measures the strength of a correlation between two variables (e.g., tenure and performance).
• Odds ratios: Often used in logistic regression to indicate how much more (or less) likely an outcome is, based on a predictor (e.g., the likelihood of promotion based on completing a leadership course).

p-Values vs. Effect Sizes: A Practical Example in People Analytics

To better understand the distinction, let’s consider a practical example in people analytics: a study on the impact of work-from-home policies on employee engagement.

• The p-value: After analyzing the data, you find that employees who work from home have significantly higher engagement scores than those who don’t, with a p-value of 0.02. This indicates that the observed difference is statistically significant—meaning it’s unlikely that this result occurred by chance.
• The effect size: However, the effect size (Cohen’s d) is 0.15, indicating a small effect. This means that while the result is statistically significant, the actual difference in engagement between work-from-home employees and office-based employees is quite small in magnitude.

Without considering the effect size, you might overestimate the importance of the work-from-home policy on engagement, when in reality, the impact is modest at best. In this case, while the p-value shows that the effect is unlikely to be random, the effect size reveals that it may not be practically significant.

The Limitations of p-Values

Many people analytics professionals rely heavily on p-values to draw conclusions from their data. However, p-values alone can be misleading, especially in large datasets. In people analytics, where employee datasets are often substantial, very small effects can produce statistically significant p-values even if the effect has little practical importance.

Limitations of p-values include:

• Sensitivity to sample size: In large datasets, even trivial effects can produce very small p-values, leading to “statistical significance” for results that aren’t meaningful in practice.
• No indication of effect size: A p-value doesn’t tell you how large or important an effect is—only that it’s unlikely to have occurred by chance.
• False positives: Relying on p-values alone increases the risk of drawing incorrect conclusions, especially if multiple comparisons are being made (a phenomenon known as the multiple comparisons problem).

For HR decision-makers, the takeaway is clear: statistical significance doesn’t always mean practical significance. To understand whether a finding has real-world implications, you must also consider the effect size.

Why Effect Sizes Matter in People Analytics

While p-values help you determine whether an effect is statistically significant, effect sizes are crucial for understanding the practical significance of your findings. In people analytics, where decisions can affect hiring, retention, promotion, and organizational culture, it’s essential to know not only whether something matters but how much it matters.

Key reasons to prioritize effect sizes:

• Actionable insights: Effect sizes provide context for decision-making. A small effect may not warrant organizational changes, whereas a large effect could lead to significant interventions.
• Comparing interventions: Effect sizes allow HR teams to compare the impact of different programs or policies. For example, if one leadership program has a larger effect size on employee engagement than another, it may be worth investing more resources into scaling that program.
• Interpreting practical outcomes: Knowing the magnitude of an effect helps stakeholders understand what to expect in terms of outcomes. For instance, an effect size that predicts a 2% improvement in retention may not be compelling enough to justify implementing a costly new initiative.

When to Use p-Values vs. Effect Sizes in People Analytics

Both p-values and effect sizes are valuable tools, but they serve different purposes in the context of people analytics. Here’s when to use each:

Use p-values:

• When determining whether a relationship or difference between variables is statistically significant (i.e., unlikely to be due to random chance).
• In hypothesis testing, where you need to reject or accept the null hypothesis.
• To assess whether your sample results can be generalized to the larger population.

Use effect sizes:

• When assessing the practical impact or importance of an effect (e.g., how much a training program improves performance).
• For comparing the magnitude of different interventions or variables (e.g., comparing the effects of work-from-home policies and leadership programs on employee engagement).
• To communicate the real-world implications of your findings to HR stakeholders.

Conclusion: Finding the Balance in People Analytics

In people analytics, both p-values and effect sizes play important roles, but they should be used in tandem to make informed, data-driven decisions. P-values help determine whether a result is statistically significant, while effect sizes tell us how large or impactful that result is in practical terms. For analysts, the key is to not stop at the p-value. Always consider the effect size to ensure that your insights are both statistically and practically significant. In doing so, you’ll be better equipped to guide decisions that improve organizational outcomes and foster a data-driven culture in HR.

At DataSkillUp (DSU), we specialize in helping people analysts develop the skills needed to interpret both p-values and effect sizes effectively. If you’re looking to deepen your understanding of statistical methods and make better HR decisions, reach out to DSU for personalized coaching and training.

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