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Purpose: The purpose of this standard is to ensure that statistical analyses‚ inferences‚ and comparisons used to develop information products are based on statistically sound practices..
Scope: The Census Bureau's statistical quality standards apply to all information products released by the Census Bureau and the activities that generate those products‚ including products released to the public‚ sponsors‚ joint partners‚ or other customers. All Census Bureau employees and Special Sworn Status individuals must comply with these standards‚ including contractors and other individuals who receive Census Bureau funding to develop and release Census Bureau information products.
In particular‚ this standard applies to the analyses performed to generate information products. It includes analyses:
The global exclusions to the standards are listed in the Preface. No additional exclusions apply to this standard.
Key Terms: Bonferroni correction‚ cluster‚ covariance‚ direct comparison‚ goodness–of–fit‚ hypothesis testing‚ implied comparison‚ multivariate analysis‚ outliers‚ parameter‚ peer review‚ regression‚ sample design‚ Scheffe’s method‚ sensitivity analysis‚ significance level‚ statistical inference‚ and Tukey’s method.
Requirement E1–1: Throughout all processes associated with analyzing data‚ unauthorized release of protected information or administratively restricted information must be prevented by following federal laws (e.g.‚ Title 13‚ Title 15‚ and Title 26)‚ Census Bureau policies (e.g.‚ Data Stewardship Policies)‚ and additional provisions governing the use of the data (e.g.‚ as may be specified in a memorandum of understanding or data–use agreement). (See Statistical Quality Standard S1‚ Protecting Confidentiality.)
Requirement E1–2: A plan must be developed prior to the start of the analysis that addresses‚ as appropriate:
Requirement E1–3: Statistically sound practices that are appropriate for the research questions must be used when analyzing the data.
Examples of statistically sound practices include:
Sub–Requirement E1–3.1: The data analysis must account for the sample design (e.g.‚ unequal probabilities of selection‚ stratification‚ and clustering) and estimation methodology.
Requirement E1–4: Any conclusions derived from sample data must be supported by appropriate measures of statistical uncertainty.
Examples of measures of statistical uncertainty that support conclusions include:
Note: Although the p–value does not indicate the size of an effect (or the size of the difference in a comparison)‚ p–valuse below 0.01 constitute strong evidence against the null‚ p–values between 0.01 and 0.05 constitute moderate evidence‚ and p–values between 0.05 and 0.10 constitute weak evidence.
Sub–Requirement E1–4.1: The same significance level or confidence level must be used throughout an analysis. Table A shows the requirements for specific information products:
Table A: Significance and Confidence Levels by Information Product
|Information Product||Significance Level||Confidence Level|
|Census Bureau publications||0.10||0.90|
|All other information products (e.g.‚ working papers‚ professional papers‚ and presentations)||0.10 or less||0.90 or more|
Requirement E1–5: The data and underlying assumptions must be appropriate for the analyses and the accuracy of the computations must be verified.
Examples of activities to check the appropriateness of the data and underlying assumptions and the accuracy of the computations:
Requirement E1–6: Documentation needed to replicate and evaluate the analysis must be produced. The documentation must be retained‚ consistent with applicable policies and data–use agreements‚ and must be made available to Census Bureau employees who need it to carry out their work. (See Statistical Quality Standard S2‚ Managing Data and Documents.)
Examples of documentation include: