Hunting bag statistics are often the only available data for performing ecological studies about harvested species, and total harvest is sometimes used as a proxy of abundance of the game species under study in a given geographical area and period of time. This practice raises at least two questions, (i) are the total hunting bag estimates good indices of population abundance, and if so, for what uses?, (ii) what is the reliability of given hunting bag statistics and is it possible to evaluate and take into account their uncertainty without relying on uncheckable assumptions? This methodological paper is aimed at answering the second question, from the point of view of the hunters' sampling. Through Monte Carlo simulations, we illustrate the potential selection bias induced by relying on volunteer samples of hunters. We expose the statistical causes and remedies to this issue. We put the emphasis on the paramount importance of random sampling, both for avoiding selection bias and to perform statistical inferences on a sound basis, in a framework free of statistical assumptions. We explain under what circumstances not taking into account unequal inclusion probabilities at the estimation stage could result in biased estimation. The acknowledgement that for a selection bias to occur, it is necessary that both the unequal inclusion probabilities are not accounted for in the estimators and these probabilities are correlated to the individual hunting bags is a statistical result that is neither widely known nor appreciated by most wildlife ecologists — and perhaps also, some wildlife statisticians.

狩猎袋统计数据通常是进行有关收获物种的生态研究的唯一可用数据，有时总收获有时被用作指定地理区域和时间段内正在研究的猎物物种丰富度的替代指标。这种做法至少引起两个问题：（i）狩猎袋的总数量是否能很好地估计种群数量指标？如果是，它的用途是什么？（ii）给出的狩猎袋统计数据的可靠性如何？有可能进行评估吗？并考虑其不确定性而不必依赖不可遏制的假设？该方法论论文旨在从猎人的抽样角度回答第二个问题。通过蒙特卡洛模拟，我们说明了依靠猎人的自愿者样本引起的潜在选择偏差。我们对此问题公开了统计原因和补救措施。我们将重点放在随机抽样的最重要意义上，既要避免选择偏差，又要在没有统计假设的框架下在合理的基础上进行统计推断。我们解释了在什么情况下在估计阶段未考虑不平等的包含概率会导致估计偏差。承认对于选择偏见的发生，有必要在估计量中不考虑两个不相等的包含概率，并且将这些概率与各个猎物袋相关联，这是大多数野生生物既不广为人知也不为人所知的统计结果生态学家，也许还有一些野生生物统计学家。我们将重点放在随机抽样的最重要意义上，既要避免选择偏差，又要在没有统计假设的框架下在合理的基础上进行统计推断。我们解释了在什么情况下在估计阶段未考虑不平等的包含概率会导致估计偏差。承认对于选择偏见的发生，有必要在估计量中不考虑两个不相等的包含概率，并且将这些概率与各个猎物袋相关联，这是大多数野生生物既不广为人知也不为人所知的统计结果生态学家，也许还有一些野生生物统计学家。我们将重点放在随机抽样的最重要意义上，既要避免选择偏差，又要在没有统计假设的框架下在合理的基础上进行统计推断。我们解释了在什么情况下在估计阶段未考虑不平等的包含概率会导致估计偏差。承认对于选择偏见的发生，有必要在估计量中不考虑两个不相等的包含概率，并且将这些概率与各个猎物袋相关联，这是大多数野生生物既不广为人知也不为人所知的统计结果生态学家，也许还有一些野生生物统计学家。在没有统计假设的框架中。我们解释了在什么情况下在估计阶段未考虑不平等的包含概率会导致估计偏差。承认对于选择偏见的发生，有必要在估计量中不考虑两个不相等的包含概率，并且将这些概率与各个猎物袋相关联，这是大多数野生生物既不广为人知也不为人所知的统计结果生态学家，也许还有一些野生生物统计学家。在没有统计假设的框架中。我们解释了在什么情况下在估计阶段未考虑不平等的包含概率会导致估计偏差。承认对于选择偏见的发生，有必要在估计量中不考虑两个不相等的包含概率，并且将这些概率与各个猎物袋相关联，这是大多数野生生物既不广为人知也不为人所知的统计结果生态学家，也许还有一些野生生物统计学家。