This paper demonstrates that rejecting the standard definition of full-time and part-time workers, the estimated number of hours that an individual is likely to work as a full-time worker is a function of the type of distribution one assumes about the error term in the wage equation. Adopting a switching regression model with unknown sample selection, we have found that the normality assumption generates higher hours for full-timers in comparison with the non-normal distributions. We also noted that regardless of the distribution assumed, the hours differ from one industry to another. The implication is that the standard definition of full-time and part-time worker may not be appropriate for all firms irrespective of the distribution assumed. The paper also shows the sensitivity of parameter estimates to the distributional assumptions about the error term in the wage equation. The results indicate that the normal distribution wage equation estimates are relatively larger than the Weibull and exponential distributions. This finding is particularly important because such differences in estimated coefficients may have a direct wage influence on the wage gap between full-time and part-time workers across distributions.