Objectives: Exposure-outcome studies, for instance on work-related low-back pain (LBP), often classify workers into groups for which exposures are estimated from measurements on a sample of workers within or outside the specific study. The present study investigated the influence on bias and power in exposure-outcome associations of the sizes of the total study population and the sample used to estimate exposures. Methods: At baseline, lifting, trunk flexion, and trunk rotation were observed for 371 of 1131 workers allocated to 19 a-priori defined occupational groups. LBP (dichotomous) was reported by all workers during 3 years of follow-up. All three exposures were associated with LBP in this parent study (P < 0.01). All 21 combinations of n = 10, 20, 30 workers per group with an outcome, and k = 1, 2, 3, 5, 10, 15, 20 workers actually being observed were investigated using bootstrapping, repeating each combination 10000 times. Odds ratios (OR) with P values were determined for each of these virtual studies. Average OR and statistical power (P < 0.05 and P < 0.01) was determined from the bootstrap distributions at each (n, k) combination. Results: For lifting and flexed trunk, studies including n ≥ 20 workers, with k ≥ 5 observed, led to an almost unbiased OR and a power >0.80 (P level = 0.05). A similar performance required n ≥ 30 workers for rotated trunk. Small numbers of observed workers (k) resulted in biased OR, while power was, in general, more sensitive to the total number of workers (n). Conclusions: In epidemiologic studies using a group-based exposure assessment strategy, statistical performance may be sufficient if outcome is obtained from a reasonably large number of workers, even if exposure is estimated from only few workers per group.