How Can Skewed Data Impact Recruitment Decisions and How Do You Correct It?

Skewed data can significantly distort recruitment analytics, leading to biased hiring decisions, flawed candidate assessments, and inaccurate salary benchmarking. Applying data transformation techniques is essential for human resources (HR) professionals to correct these asymmetries and build fairer, more effective hiring models. Understanding skewness—a measure of asymmetry in a data distribution—is critical for interpreting recruitment metrics correctly.

What is Skewness in Recruitment Data?

In recruitment, skewness describes the asymmetry in a set of data, such as candidate scores, salary figures, or time-to-hire metrics. A normal, symmetrical distribution has a skewness value of zero, meaning the data is balanced. However, real-world HR data is rarely perfect. There are two primary types of skewness:

• Positive Skewness: The "tail" of the distribution extends to the right. For example, a department's salary data is positively skewed if most employees are clustered at the lower end of the pay band, with a few high-earning executives pulling the average salary upward.
• Negative Skewness: The "tail" extends to the left. This might be seen in an overly easy pre-employment test where most candidates score very high, making the distribution skewed left.

Recognizing this asymmetry is the first step in ensuring your data-driven decisions are based on a realistic picture.

Why is Identifying Skewness Crucial in Hiring?

Ignoring skewness in recruitment data can lead to several problems. Predictive models used in candidate screening often assume that data is normally distributed. When this assumption is violated by skewness, the model's accuracy decreases. For instance, if your data on a key competency is skewed, the model might undervalue or overvalue certain candidate profiles.

Furthermore, skewness helps identify potential biases or outliers. A negatively skewed distribution of performance ratings within a team could indicate leniency bias from a manager. By investigating the cause of the skew, HR can take corrective action, such as providing rater training. According to standards from institutions like the Society for Human Resource Management (SHRM), ensuring the integrity of your data is fundamental to equitable hiring practices.

How Can You Correct Positively Skewed Recruitment Data?

Positive skewness, where most data points are on the left with a tail to the right, is common with metrics like salary or time-to-fill a position. To normalize this data for analysis, HR analysts can use data transformation techniques. The goal is to make the distribution more symmetrical, which improves the performance of statistical models.

Based on our assessment experience, the logarithmic transformation is particularly effective for salary data, as it compresses the extreme high values and brings the distribution closer to normal.

What Are the Methods for Fixing Negatively Skewed Data?

Negatively skewed data, which has a tail extending to the left, can be corrected using power transformations. These methods work by stretching the values on the lower end. For example, if candidate scores on an assessment are negatively skewed (most candidates scoring very high), applying a square ((x^2)) or cube ((x^3)) transformation can help spread out the lower scores and create a more balanced distribution. This leads to a more discriminative analysis, helping to better identify the top performers from the adequately skilled candidates.

To ensure your recruitment strategies are driven by accurate data, start by visually examining your key metrics for asymmetry. Correcting skewness is not about manipulating outcomes but about refining your data to reveal true trends and mitigate hidden biases.