Occupational Stress & Sleep Pathology Analytics: Inferential Evaluation of Stress, BMI, and Demographic Determinants on Sleep Metrics among Working Adults

This project delivered an end-to-end inferential data science investigation into the impacts of occupational stress, professional roles, gender dynamics, and body mass index (BMI) on sleep quality and clinical sleep disorder prevalence among working adults. Analyzing the Sleep Health and Lifestyle Dataset ($N = 374$ records reduced across 8 primary demographic and clinical variables), the study formulated a quantitative pipeline in Python utilizing pandas, scipy.stats, matplotlib, and seaborn to execute descriptive and inferential hypothesis testing without relying on black-box machine learning models. Initial data hygiene addressed structural redundancies by standardizing duplicate BMI labels (merging "Normal Weight" into "Normal") and recoding unpopulated diagnostic fields as non-pathological baseline cases ("None"), reflecting the explicit absence of diagnosed clinical disorders rather than missing data.The exploratory and statistical analysis evaluated five core hypotheses to delineate the primary drivers of sleep degradation. Bivariate correlation testing revealed an exceptionally strong inverse relationship between self-reported stress levels and sleep quality (Pearson $r = -0.899, p < 0.001$; Spearman $\rho = -0.908, p < 0.001$), as well as total sleep duration (Pearson $r = -0.811, p < 0.001$; Spearman $\rho = -0.811, p < 0.001$), confirming that psychological strain severely suppresses sleep metrics across population strata. Variance analysis across eleven distinct professional categories demonstrated significant inter-occupational disparities in sleep quality (One-Way ANOVA $F = 30.022, p < 0.001$; Kruskal-Wallis $H = 166.094, p < 0.001$). High median sleep quality was reported among Engineers and Nurses, whereas marked reductions were observed in high-pressure roles such as Sales Representatives and Scientists. Gender-stratified independent Welch's t-tests revealed a statistically significant disparity ($t = -5.859, p < 0.001$), with female respondents reporting higher mean sleep quality ($7.66$) compared to male counterparts ($6.97$).Categorical association testing via Chi-Square ($\chi^2$) tests of independence demonstrated profound interactions between physical health indicators, occupational environments, and clinical sleep pathologies. BMI category showed a highly significant association with sleep disorder diagnosis ($\chi^2 = 245.665, df = 4, p < 0.001$), where individuals with normal BMI were overwhelmingly free of diagnosed conditions ($200/216$), while overweight and obese groups exhibited heightened susceptibility to both Insomnia ($n = 64$) and Sleep Apnea ($n = 65$). Furthermore, occupation was significantly tied to specific clinical diagnoses ($\chi^2 = 421.363, df = 20, p < 0.001$), uncovering domain-specific risk clusters: Nurses suffered disproportionately high rates of Sleep Apnea ($61/73$), whereas Salespeople ($29/32$) and Teachers ($27/40$) displayed acute clustering of Insomnia. Supported by a publication-ready visual suite—including linear regression trend plots, occupational boxplots, stacked bar charts, and correlation heatmaps—the study delivers empirical evidence to inform targeted workplace wellbeing policies and shift-work health interventions.