Mathematical proofs show that rate estimates, for example of mortality and recruitment, will decrease with increasing census interval when obtained from censuses of non-homogeneous populations. This census interval effect could be confounding or perhaps even driving conclusions from comparative studies involving such rate estimates. We quantify this artefact for tropical forest trees, develop correction methods and reassess some previously published conclusions about forest dynamics. Mortality rates of >50 species at each of seven sites in Africa, Latin America, Asia and Australia were used as subpopulations to simulate stand level mortality rates in a heterogeneous population when census intervals varied: all sites showed decreasing stand mortality rates with increasing census interval length. Stand-level mortality rates from 14 multicensus long-term forest plots from Africa, Latin America, Asia and Australia also showed that, on average, mortality rates decreased with increasing census interval length. Mortality, recruitment or turnover rates with differing census interval lengths can be compared using the mean rate of decline from the 14 long-term plots to standardize estimates to a common census length using the expression corr= x t^0.08, where is the rate and t is time between censuses in years: i.e., [corrected for time bias] = [as derived from simple analyses]x( time[=years between measurements] to the power of 0.08). This simple general correction should reduce the biasassociated with census interval variation, where it is unavoidable. Re-analysis of published results shows that the pan-tropical increase in stem turnover rates over the late 20th century cannot be attributed to combining data with differing census intervals. In addition, after correction, Old World tropical forests do not have significantly lower turnover rates than New World sites, as previously reported. Our pan-tropical best estimate adjusted stem turnover rate is 1.81 +/- 0.16% per year (mean +/- 95% CI, n=65). As differing census intervals affect comparisons of mortality, recruitment and turnover rates, and can lead to erroneous conclusions, standardized field methods, the calculation of local correction factors at sites where adequate data are available, or the use of our general standardizing formula to take account of sample intervals, are to be recommended.