Recent investigations of submillennial paleoceanographic variability have attempted to resolve high-frequency climate signals such as the El Niño Southern Oscillation (ENSO) using the population statistics of individual planktic foraminiferal δ18O analyses. This approach is complicated by the relatively short lifespan of individual foraminifers (~2–4 weeks) compared to the time represented by a typical marine sediment sample (~decades to millennia). Here, we investigate the uncertainty associated with individual foraminiferal analyses (IFA) through simulations on forward modeled δ18Ocarbonate. First, focusing on the Niño3.4 region of the tropical Pacific Ocean, a bootstrap Monte Carlo algorithm is developed to constrain the uncertainty on IFA-statistics. Subsequently, to test the sensitivity of IFA to changes in seasonal cycle amplitude, ENSO amplitude, and ENSO frequency, synthetic time series of δ18Ocarbonate with differing variability are constructed and tested with our algorithm. The probabilities of the IFA technique in detecting changes in ENSO amplitude and seasonal cycle amplitude (or a combination of both) for the surface ocean and thermocline at different locations in the tropical Pacific are quantified. We find that the uncertainty in the standard deviation is smaller than the range, that the IFA-signal is insensitive to ENSO frequency, and at certain locations the seasonal cycle may dominate ENSO. IFA sensitivity towards ENSO is highest at the central equatorial Pacific surface ocean and the eastern equatorial Pacific (EEP) thermocline whereas sensitivity towards the seasonal cycle is highest at the EEP surface ocean. Our results suggest that rigorous uncertainty quantification should become standard practice for accurately interpreting IFA-data.