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The thesis investigates the impact of age-biased sampling on the accuracy of lifetime prediction models for traffic signs. The bias in question originates from age-biased sampling as a result of the inspection paradox. This phenomenon occurs because longer intervals have a higher probability of being observed compared to shorter intervals, leading to a skewed representation in the data. The research employs a dual approach: firstly, conducting an extensive analysis of real data on traffic sign longevity using a Weibull Survival Model. This analysis is based on the data set compiled by Saleh et al., (2023). Secondly, the study sets up a Monte Carlo simulation to systematically explore the effects of varying degrees and patterns of age bias on the sample. The simulation parameters are derived from the original Weibull Model parameters, obtained from the real dataset. This approach ensures that the simulations closely replicate the actual parameters and estimates. The comparison of the true shape, scale, intercept, and the coefficients associated with the covariates against the simulated estimates indicates a significant bias in the dataset. The study also examines the impact of this bias on the predictive capabilities of various models: Weibull Modeling, Cox Proportional Hazards, Kaplan Meier, and Random Survival Forest. This is done by comparing the true means and medians of the simulated data with the estimates from each model. The findings show that all models exhibit large deviations from the actual means and medians at varying bias levels in the simulated data. The accuracy of the predictions is measured using the Brier Score. This score also shows significant deviations from the prediction accuracy of the original Weibull Model applied to the real dataset, especially when the bias levels vary across simulated datasets. Given these findings, the study advises against using the aforementioned methods for lifetime modeling of traffic signs when there is age bias due to the inspection paradox.