Sampling Analysis

• Tier: supplementary
• Observed in 173 of 242 proposals (71.5%).
• Median relative position: 0.421.
• Median length units: 8.0.
• Canonical core candidate: false.

When to use

Write this section when a solicitation asks for sampling analysis content, or when the RFP's own outline has a slot in this position. Observed in 71.5% of proposals, at roughly position 0.421 of the document, which is where reviewers look for it.

Read the style guide and do-not-fabricate before drafting. Requirements in the current RFP override everything on this page.

These are corpus observations, not mandatory defaults. Current RFP requirements control.

Evidence

• Sampling Expertise [F000493, paragraph:327]
• Statistical Analysis Ability [F000493, paragraph:329]
• Statistical Analysis and Sampling [F000493, paragraph:336]
• Statistical Analysis [F000493, paragraph:349]
• Sampling Expertise and Analysis of Ex post Net Program Effects [F000493, paragraph:358]
• Statistical Analysis, Sample Selection, Billing Analysis [F000493, paragraph:407]
• Sample Design [F000493, paragraph:453]
• Energy Use & Statistical Analysis [F000493, paragraph:628]
• 3.4.2.6 Sampling Approach and Sample Plans (Page 106) [F000493, paragraph:766]
• Statistical Analysis of Billing Data [F000493, paragraph:794]
• Sampling Approach and Sample Plans [F000493, paragraph:947]
• Impact Evaluation Sampling Approach [F000493, paragraph:949]
• Process Evaluation Sampling Approach [F000493, paragraph:957]
• Specify Criteria for Sampling Plans [F000554, paragraph:313]
• 2.3.1. Sample Design [F000815, page:15:line:31]
• Statistical Analysis [F000815, page:38:line:17]
• 6. Appendix A: Preliminary Sample Design [F000815, page:42:line:1]
• Appendix A: Preliminary Sample Design 38 [F000815, page:42:line:34]
• Appendix A: Preliminary Sample Design 39 [F000815, page:43:line:37]
• Subtask 2.1. Develop Sampling Strategy [F000844, page:17:line:33]

Exemplars

Specify Criteria for Sampling Plans Evaluation of energy efficiency and demand reduction programs requires solid data on important customer attributes. However, data collection has been characterized as the “Achilles heel” of program evaluation. Care needs to be taken in choosing samples of customers for the analysis and in collecting data on the characteristics of these customers.

Source: [F000554, paragraph:313]

Reusable Candidates

• BLK-2F5938E19338C4 - evergreen_candidate, 13 documents, approve
• BLK-B8CA92313A5F5B - evergreen_candidate, 8 documents, approve
• BLK-AD0D06D4418699 - evergreen_candidate, 7 documents, approve
• BLK-1082B5EB73A9B1 - mixed_context, 6 documents, approve
• BLK-A9C6FB13C46780 - evergreen_candidate, 6 documents, approve
• BLK-67C8520A5F898A - evergreen_candidate, 5 documents, approve
• BLK-C2D6CD99F1DE19 - evergreen_candidate, 5 documents, approve
• BLK-D84AFCC206B925 - evergreen_candidate, 5 documents, approve
• BLK-241B9E3D1BB9AF - mixed_context, 4 documents, approve
• BLK-44C2D1B5B28DE9 - mixed_context, 4 documents, approve
• BLK-9D5A09A7FECB6F - mixed_context, 4 documents, approve
• BLK-A441AE2A226975 - mixed_context, 4 documents, approve