Statistical P value and its threshold have been controversial worldwide for a while. Recent heated debate was triggered by two practical issues: unexplainable high false positive rate in biomedical research, and global misunderstood of "statistical significance" in scientific community. Thus, part of scientists suggests applying more stringent significance level (from 0.05 to 0.005), or even giving up the use of significance level. We believe that they are throwing the baby out with the bath water. These suggestions will not contribute to any improvement of this unfavorable situation but will lead the scientific decision-making to a more difficult and subjective corner. Scientists should use statistical P value and threshold only if they correctly understand the soul of statistics-uncertainty. Statistical significance is neither sole nor dominant criterion to measure the scientific value, but an honest assistant. Scientific decision-making should initiate from the scientific experimental design, followed by rigorous implementation and transparent analysis, and synthesize a variety of information to reach a tenable conclusion.
近年来，统计学假设检验的P值饱受争议。源于两个现实问题：一是生物医学研究中假阳性率过高；二是假设检验"统计学意义"被误解和误用。为此，部分学者建议将检验水准从常用的0.05降低至0.005，甚至建议放弃使用检验水准。笔者认为，如果不能正确理解P的意义，并以严谨的态度对待研究，无论是降低检验水准，还是取消检验水准，都不会改变假阳性过高的现状。科学工作者只有正确理解了P值和检验水准的含义，才能运用自如。做决策时，不能将"统计学意义"作为衡量是否有科学意义的唯一标准，即科学研究不唯P。应从实验设计、实施、分析的科学性、严谨性、规范性出发，综合多种信息，进行科学决策。.
Keywords: P values; Significance level; Statistics.