Across more than 200 organizations in coordinated multi-country four-day-week trials (2022-2025), employee well-being outcomes - reduced burnout, stress, and fatigue, plus improved job and life satisfaction - improved sharply and consistently, and these gains largely held at 12-month follow-up.
Well-being gains were stable at a 12-month follow-up rather than fading once the novelty wore off.
In the pooled six-country study of income-preserving four-day weeks (Fan, Schor et al., Nature Human Behaviour 2025), workers reported improved mental and physical health, higher job satisfaction, and reduced burnout, with gains partly driven by reduced fatigue and fewer sleep problems.
The German national trial corroborated self-reported well-being gains with objective measures - wearable-tracked sleep (+38 min/week), heart-rate, and hair-cortisol analysis - reducing reliance on self-report alone for the well-being outcomes.
Argument from Sample to Population (Statistical Generalization)
Does the operational measure of F in the sample actually capture F as it is meant in the population-level claim?
Is the sample actually representative of the target population on the dimensions that matter for F (demographics, behavior, time period, platform mix)?
Is the sample large enough to support the precision (margin m) being claimed?
Does the conclusion stay within the population P from which S was drawn, or does it overreach (different country, different time period, different platform)?
Was the sample drawn or recruited in a way that systematically biases the proportion of F (e.g., volunteer bias, opt-in panels, attrition)?