How to Interpret Descriptive Statistics in Workforce Salary Analysis

Descriptive statistics play a foundational role in workforce and labour market analysis. Before applying regression models or causal inference techniques, analysts must first understand the structure, distribution, and key characteristics of the data. This article explains how descriptive tables can be used to examine salary patterns within an organisation and highlights how differences across employee groups can be identified.

Understanding Variable Types in Workforce Data

A critical first step in any data analysis is distinguishing between continuous and categorical variables.

In workforce datasets, variables such as annual salary and years of experience are typically continuous, while characteristics like job position, gender, and minority status are categorical. Correct classification ensures that appropriate summary measures and visualisations are used during analysis.

Interpreting Descriptive Statistics for Continuous Variables

Descriptive statistics such as the mean, standard deviation, minimum, and maximum provide insight into the distribution of numerical variables.

For salary data, the mean indicates average earnings, while the standard deviation reflects income dispersion within the organisation. Large differences between minimum and maximum values often suggest hierarchical wage structures, especially where managerial roles are present.

Similarly, years worked and education levels help contextualise salary outcomes by capturing experience and human capital differences across employees.

Analysing Categorical Variables Using Frequency Tables

Categorical variables are best summarised using frequency and percentage distributions.

Job position categories often reveal workforce composition, such as a large proportion of clerical staff relative to managerial roles. Binary variables like gender and minority status allow analysts to examine representation and assess whether outcomes differ across demographic groups.

Frequency tables provide a clear snapshot of organisational structure before deeper analysis is conducted.

Comparing Group Means

One of the most informative descriptive tools is the comparison of group averages. By calculating mean salary, experience, and education levels across different categories, analysts can observe systematic differences.

For example:

• Average salaries often differ substantially across job positions, reflecting responsibility and skill requirements.

• Gender-based salary differences may appear even when experience levels are similar.

• Minority and non-minority groups may differ in both earnings and educational attainment.

These comparisons do not establish causality but highlight areas where further investigation may be warranted.

Identifying Patterns and Potential Disparities

When consistent differences appear across multiple group comparisons, they suggest structural patterns within the organisation. Salary gaps aligned with gender, minority status, or job category may indicate unequal access to higher-paying roles, differences in promotion pathways, or historical inequalities.

At this stage, the role of descriptive analysis is to flag patterns, not to assign explanations. Further statistical modelling is typically required to control for confounding factors.

Role of Descriptive Analysis in Applied Statistics

Descriptive statistics serve as the bridge between raw data and formal econometric analysis. They help analysts:

• Detect anomalies or data quality issues

• Understand workforce composition

• Frame meaningful research questions

• Justify the choice of subsequent analytical methods

Without a clear descriptive foundation, advanced statistical results can be misleading or misinterpreted.

Descriptive statistics provide essential insight into workforce salary data by summarising distributions and highlighting group-level differences. Through careful interpretation of means, variability, and categorical breakdowns, analysts can identify key patterns that inform deeper investigation into labour market outcomes and organisational equity.

Check-In 1

Table 1: Variable Description Table

Table 2: Descriptive Statistics Table

Table 3: Categorical Variables Summary

Summary by Sex:

Summary by Position:

Summary by Minority Status:

Q4. The data reveal that managers earn significantly more than clerical or custodial staff, males earn more than females on average, and non-minority employees tend to have higher salaries and education levels than minority employees. These patterns suggest potential disparities related to position, gender, and minority status.