更改

为了了解和克服这些偏见，分析师可能会接受专门培训。退休的美国中情局分析师Richards Heuer在他的《'''<font color = '#ff8000'>情报分析心理学Psychology of Intelligence Analysis</font>'''》一书中写道，分析师应该清楚地描述他们的预设和推断链，明确结论中包含的不确定性的程度和来源。他强调有助于提出和辩论不同观点的程序。<ref name="Heuer1">{{cite web|url=https://www.cia.gov/library/center-for-the-study-of-intelligence/csi-publications/books-and-monographs/psychology-of-intelligence-analysis/art3.html|title=Introduction|work=cia.gov}}</ref>

为了了解和克服这些偏见，分析师可能会接受专门培训。退休的美国中情局分析师Richards Heuer在他的《'''<font color = '#ff8000'>情报分析心理学Psychology of Intelligence Analysis</font>'''》一书中写道，分析师应该清楚地描述他们的预设和推断链，明确结论中包含的不确定性的程度和来源。他强调有助于提出和辩论不同观点的程序。<ref name="Heuer1">{{cite web|url=https://www.cia.gov/library/center-for-the-study-of-intelligence/csi-publications/books-and-monographs/psychology-of-intelligence-analysis/art3.html|title=Introduction|work=cia.gov}}</ref>

===Innumeracy 数学盲===

===数学盲===

Effective analysts are generally adept with a variety of numerical techniques. However, audiences may not have such literacy with numbers or numeracy; they are said to be innumerate. Persons communicating the data may also be attempting to mislead or misinform, deliberately using bad numerical techniques.

高效的分析师通常善于使用各种数字的技术。然而，受众可能没有这样的数字和算术读写能力；他们被认为是'''<font color='#ff8000'>数学盲Innumeracy</font>'''。或者是传递数据的人的数据分析技术过于糟糕，会导致出现'''<font color='#ff8000'>误导mislead</font>'''或'''<font color='#ff8000'>误报misinform</font>'''的情况。<ref>[http://www.bloombergview.com/articles/2014-10-28/bad-math-that-passes-for-insight Bloomberg-Barry Ritholz-Bad Math that Passes for Insight-October 28, 2014]</ref>

For example, whether a number is rising or falling may not be the key factor. More important may be the number relative to another number, such as the size of government revenue or spending relative to the size of the economy (GDP) or the amount of cost relative to revenue in corporate financial statements. This numerical technique is referred to as normalization or common-sizing. There are many such techniques employed by analysts, whether adjusting for inflation (i.e., comparing real vs. nominal data) or considering population increases, demographics, etc. Analysts apply a variety of techniques to address the various quantitative messages described in the section above.

例如，一个数是上升还是下降可能不是关键因素。更重要的可能是相对于另一个数的数，例如相对于经济规模（国内生产总值）的政府收入或支出，或者公司财务报表中相对于收入的成本金额。这种数的技术称为'''归一化normalization'''<ref name="Koomey1"/> 或'''<font color='#ff8000'>共同比common-sizing</font>'''。分析师们通常都会使用这样的数据分析技术来进行调整，无论是对通货膨胀进行调整（如，比较实际数据与名义上的数据） ，还是考虑人口增长、人口统计学信息等。