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==Barriers to effective analysis==

==Barriers to effective analysis 有效分析的障碍==

Barriers to effective analysis may exist among the analysts performing the data analysis or among the audience. Distinguishing fact from opinion, cognitive biases, and innumeracy are all challenges to sound data analysis.

Barriers to effective analysis may exist among the analysts performing the data analysis or among the audience. Distinguishing fact from opinion, cognitive biases, and innumeracy are all challenges to sound data analysis.

Barriers to effective analysis may exist among the analysts performing the data analysis or among the audience. Distinguishing fact from opinion, cognitive biases, and innumeracy are all challenges to sound data analysis.

Barriers to effective analysis may exist among the analysts performing the data analysis or among the audience. Distinguishing fact from opinion, cognitive biases, and innumeracy are all challenges to sound data analysis.

===Confusing fact and opinion===

===Confusing fact and opinion 混淆事实和观点===

{{quote box|quote=You are entitled to your own opinion, but you are not entitled to your own facts.|source=[[Daniel Patrick Moynihan]]|width = 250px}}

{{quote box|quote=You are entitled to your own opinion, but you are not entitled to your own facts.|source=[[Daniel Patrick Moynihan]]|width = 250px}}

Effective analysis requires obtaining relevant facts to answer questions, support a conclusion or formal opinion, or test hypotheses. Facts by definition are irrefutable, meaning that any person involved in the analysis should be able to agree upon them. For example, in August 2010, the Congressional Budget Office (CBO) estimated that extending the Bush tax cuts of 2001 and 2003 for the 2011–2020 time period would add approximately $3.3 trillion to the national debt. Everyone should be able to agree that indeed this is what CBO reported; they can all examine the report. This makes it a fact. Whether persons agree or disagree with the CBO is their own opinion.

Effective analysis requires obtaining relevant facts to answer questions, support a conclusion or formal opinion, or test hypotheses. Facts by definition are irrefutable, meaning that any person involved in the analysis should be able to agree upon them. For example, in August 2010, the Congressional Budget Office (CBO) estimated that extending the Bush tax cuts of 2001 and 2003 for the 2011–2020 time period would add approximately $3.3 trillion to the national debt. Everyone should be able to agree that indeed this is what CBO reported; they can all examine the report. This makes it a fact. Whether persons agree or disagree with the CBO is their own opinion.

有效的分析需要获得相关的事实来回答问题，支持结论或正式的意见，或者检验假设。事实的定义是不可辩驳的，这意味着任何参与分析的人都应该能够同意它们。例如，2010年8月，国会预算办公室(CBO)估计，延长布什2001年和2003年的2011-2020年减税政策将使国家债务增加约3.3万亿美元。每个人都应该能够同意，这确实是国会预算办公室报告的; 他们都可以检查报告。这使它成为一个事实。人们是否同意国会预算办公室是他们自己的观点。

有效的分析需要获得相关的事实来回答问题、支持结论、支持正式的观点，或者检验假设。事实的定义是不可辩驳的，这意味着任何参与分析的人都应该能够同意它们。例如，2010年8月，'''<font color='#ff8000'>美国国会预算办公室Congressional Budget Office(CBO)</font>'''估计，延长布什2001年和2003年的2011-2020年减税政策将使国家债务增加约3.3万亿美元。每个人都应该能够同意这确实是国会预算办公室报告的；他们都可以检查报告。这使该报告的内容成为一个事实。人们可以选择自己观点：是否同意国会预算办公室的报告。

As another example, the auditor of a public company must arrive at a formal opinion on whether financial statements of publicly traded corporations are "fairly stated, in all material respects." This requires extensive analysis of factual data and evidence to support their opinion. When making the leap from facts to opinions, there is always the possibility that the opinion is erroneous.

As another example, the auditor of a public company must arrive at a formal opinion on whether financial statements of publicly traded corporations are "fairly stated, in all material respects." This requires extensive analysis of factual data and evidence to support their opinion. When making the leap from facts to opinions, there is always the possibility that the opinion is erroneous.

===Cognitive biases===

===Cognitive biases 认知偏差===

There are a variety of [[cognitive bias]]es that can adversely affect analysis. For example, [[confirmation bias]] is the tendency to search for or interpret information in a way that confirms one's preconceptions. In addition, individuals may discredit information that does not support their views.

There are a variety of [[cognitive bias]]es that can adversely affect analysis. For example, [[confirmation bias]] is the tendency to search for or interpret information in a way that confirms one's preconceptions. In addition, individuals may discredit information that does not support their views.

There are a variety of cognitive biases that can adversely affect analysis. For example, confirmation bias is the tendency to search for or interpret information in a way that confirms one's preconceptions. In addition, individuals may discredit information that does not support their views.

There are a variety of cognitive biases that can adversely affect analysis. For example, confirmation bias is the tendency to search for or interpret information in a way that confirms one's preconceptions. In addition, individuals may discredit information that does not support their views.

Analysts may be trained specifically to be aware of these biases and how to overcome them. In his book Psychology of Intelligence Analysis, retired CIA analyst Richards Heuer wrote that analysts should clearly delineate their assumptions and chains of inference and specify the degree and source of the uncertainty involved in the conclusions. He emphasized procedures to help surface and debate alternative points of view.

Analysts may be trained specifically to be aware of these biases and how to overcome them. In his book Psychology of Intelligence Analysis, retired CIA analyst Richards Heuer wrote that analysts should clearly delineate their assumptions and chains of inference and specify the degree and source of the uncertainty involved in the conclusions. He emphasized procedures to help surface and debate alternative points of view.

===Innumeracy===

===Innumeracy 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.<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>

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.<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>

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.

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.

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.

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.

Analysts may also analyze data under different assumptions or scenarios. For example, when analysts perform financial statement analysis, they will often recast the financial statements under different assumptions to help arrive at an estimate of future cash flow, which they then discount to present value based on some interest rate, to determine the valuation of the company or its stock.  Similarly, the CBO analyzes the effects of various policy options on the government's revenue, outlays and deficits, creating alternative future scenarios for key measures.

Analysts may also analyze data under different assumptions or scenarios. For example, when analysts perform financial statement analysis, they will often recast the financial statements under different assumptions to help arrive at an estimate of future cash flow, which they then discount to present value based on some interest rate, to determine the valuation of the company or its stock.  Similarly, the CBO analyzes the effects of various policy options on the government's revenue, outlays and deficits, creating alternative future scenarios for key measures.

 

 

==Other topics==

==Other topics==