生命表是古人口学发展早期的常用方法，但历来受到许多质疑。其问题主要有：死亡率误区和队列假设的影响，静止人口假设或稳定人口假设问题，模型生命表存在的问题，人骨年龄估计问题和抽样缺陷问题。生命表的意义主要在于其可以直观展示古人口的死亡过程和计算古人口的预期寿命。目前在古人口学中，生命表已逐渐趋于淘汰，而以风险模型为代表的其他方法成为了研究古人口死亡模式的新方法。

The life table method was the primary approach used in early phases of paleodemography work even though it attracted many sceptics. There are five major problems using this life table method. First, mistakes in estimating mortality rate was common in practice. In fact, the effect of the hypothesis of cohort often led to an unavailability of the mortality rate, which meant that could no data was obtained from archaeological sample. Second, the hypothesis of stationary or stable populations was another risk factor because of weak validity in real populations, although the hypothesis of stable populations looked more reasonable. Third, the use of model life tables was not appropriate because it specified a single mortality rate while eliminating other possibilities and reducing the value of research. Fourth, difficulties in age estimation of the human skeleton were challenging in obtaining data for the life table, including an unreliability of adult age estimation, an inability of older age estimations, and that generally that age estimation was an interval, not point, estimation. Fifth, flaws in sampling reduced representativeness of the sample of human skeletons and made sample sizes too small.

The value of life tables is that it can be used to exhibit death processes of past populations and to estimate life expectancy. Nowadays, the life table method tends to be dismissed in paleodemography, because it is a mathematical model of mortality rate, while age-at-death distribution (the only information about death provided by archaeological sample) is more likely to mirror fertility, not mortality. In recent years, the life table method has been replaced gradually by newer methods such as parametric models of mortality, specifically the hazard model as a widely recommended method. Other methods include the Cox proportional hazard model, the Kaplan-Meier survival analysis, and simulated annealing optimization. A way to promote paleodemography is to expand the research scale in time and space, which would not only avoid drawbacks of small-scale research, but also produce broader understanding of research questions.