Opportunity Information: Apply for PD 18 1269
The National Science Foundation (NSF) is offering a discretionary grant opportunity titled "Statistics" (Funding Opportunity Number: PD 18-1269) under its Science and Technology and other Research and Development activity category (CFDA: 47.049). This program is aimed at supporting research that advances the foundations and practice of statistical science, with an emphasis on both core statistical theory and the development of statistical methods that can be applied across essentially any area of science or engineering. The opportunity is designed to fund work that strengthens how researchers model randomness, learn from data, and quantify uncertainty, all of which are central to making reliable scientific and engineering advances.
The focus of the program spans two tightly connected areas: statistical theory and statistical methods. On the theory side, the program supports work that builds the mathematical and conceptual foundations of statistics, such as understanding the properties of estimators, inference procedures, and learning algorithms, and clarifying when and why particular approaches succeed or fail. This theoretical base is presented as essential to the broader field because it underpins the credibility and generalizability of statistical conclusions. On the methods side, the program supports research that creates or improves practical techniques for stochastic modeling and for the collection, analysis, and interpretation of data. In plain terms, this includes developing new tools for designing studies and experiments, extracting signal from noisy measurements, and translating data into defensible conclusions.
A central theme throughout the opportunity is uncertainty quantification. The NSF description highlights that statistical methods should not just produce answers, but should also characterize the uncertainty inherent in the data and the modeling assumptions. This can include, for example, improved methods for confidence intervals, hypothesis testing, Bayesian posterior summaries, prediction intervals, model checking, and robustness to misspecification. By treating uncertainty as a first-class output, the program aims to support research that makes downstream scientific and engineering decisions more trustworthy and better calibrated to the limits of what the data can support.
The program explicitly welcomes a wide range of project structures, from single-investigator proposals to interdisciplinary team projects. This signals that NSF is open to both focused foundational contributions and larger collaborative efforts that bring statisticians together with domain scientists or engineers. Because the solicitation emphasizes applicability to any domain of science and engineering, strong proposals could involve methodological advances motivated by real data challenges in fields such as biology, medicine, environmental science, materials, social science, cybersecurity, manufacturing, or artificial intelligence, as long as the statistical contribution is substantive and generalizable beyond one narrow case.
Eligibility is listed as unrestricted, indicating that the applicant pool is broad and not limited to a particular institution type in the provided summary fields. The original closing date shown is December 16, 2024. The listing does not provide an award ceiling or an expected number of awards in the supplied source data, so prospective applicants would need to consult the full NSF program page or the current solicitation details for up-to-date information on typical award sizes, durations, and portfolio priorities. Overall, the opportunity is best understood as support for research that advances statistical thinking and tool-building, with the long-term goal of improving how science and engineering use data to learn, predict, and make decisions under uncertainty.Apply for PD 18 1269
- The National Science Foundation in the science and technology and other research and development sector is offering a public funding opportunity titled "Statistics" and is now available to receive applicants.
- Interested and eligible applicants and submit their applications by referencing the CFDA number(s): 47.049.
- This funding opportunity was created on 2018-03-29.
- Applicants must submit their applications by 2024-12-16. (Agency may still review applications by suitable applicants for the remaining/unused allocated funding in 2026.)
- Eligible applicants include: Unrestricted.
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Frequently Asked Questions (FAQs) - NSF "Statistics" Discretionary Grant (PD 18-1269)
What is the name of this NSF grant opportunity?
The opportunity is titled "Statistics" and is offered by the National Science Foundation (NSF).
What is the Funding Opportunity Number for this program?
The Funding Opportunity Number is PD 18-1269.
What CFDA number is associated with this opportunity?
The listing references CFDA 47.049.
What activity category does this opportunity fall under?
It is listed under the "Science and Technology and other Research and Development" activity category.
What is the overall purpose of the NSF Statistics program?
The program supports research that advances both the foundations and the practice of statistical science. It emphasizes strengthening how researchers model randomness, learn from data, and quantify uncertainty so scientific and engineering conclusions are more reliable.
What kinds of research topics does the program emphasize?
The opportunity highlights two tightly connected areas: statistical theory and statistical methods, with uncertainty quantification as a central theme throughout.
What does NSF mean by "statistical theory" in this program?
On the theory side, the program supports work that builds the mathematical and conceptual foundations of statistics. Examples described include studying properties of estimators, inference procedures, and learning algorithms, and clarifying when and why particular approaches succeed or fail.
What does NSF mean by "statistical methods" in this program?
On the methods side, the program supports research that creates or improves practical techniques for stochastic modeling and for the collection, analysis, and interpretation of data. This includes work on designing studies and experiments, extracting signal from noisy measurements, and translating data into defensible conclusions.
Why is uncertainty quantification emphasized in this opportunity?
The description stresses that statistical methods should not only produce answers, but also characterize uncertainty coming from the data and from modeling assumptions. Treating uncertainty as a key output is meant to make downstream scientific and engineering decisions more trustworthy.
What are examples of uncertainty quantification topics mentioned in the description?
Examples given include improved methods for confidence intervals, hypothesis testing, Bayesian posterior summaries, prediction intervals, model checking, and robustness to model misspecification.
Does the program support only theoretical work, or only applied work?
It supports both foundational theory and practical methods. The framing suggests that theoretical contributions are valued because they underpin credibility and generalizability, while methodological advances are valued because they improve real-world data analysis and decision-making.
Are interdisciplinary or team proposals allowed?
Yes. The program explicitly welcomes a wide range of project structures, from single-investigator proposals to interdisciplinary team projects.
Can proposals involve collaboration with domain scientists or engineers?
Yes. The opportunity signals openness to collaborations that bring statisticians together with domain scientists or engineers, as long as the statistical contribution is substantive and generalizable beyond one narrow case.
What application domains can this program apply to?
The solicitation emphasizes that statistical methods can be applicable across essentially any area of science or engineering. Examples mentioned include biology, medicine, environmental science, materials, social science, cybersecurity, manufacturing, and artificial intelligence, provided the statistical contribution is broadly useful.
Does a proposal need to be motivated by real data challenges?
The description indicates that strong proposals could be motivated by real data challenges in a domain field, but it also emphasizes that the statistical contribution should be substantive and generalizable beyond a single narrow case.
Who is eligible to apply for this grant opportunity?
Eligibility is listed as unrestricted in the provided summary fields, indicating a broad applicant pool that is not limited to a particular institution type in the information provided.
What is the closing date listed for this opportunity?
The original closing date shown is December 16, 2024.
How much funding is available per award (award ceiling)?
The provided listing does not include an award ceiling. Applicants would need to consult the full NSF program page or the current solicitation for typical award sizes and related details.
How many awards does NSF expect to make under this opportunity?
The supplied source data does not state an expected number of awards. For current expectations, applicants would need to check the full NSF program page or solicitation details.
Does the provided information specify award duration or project length?
No. The provided summary does not include typical award durations or project length information, so applicants would need to confirm those details in the full NSF materials.
What is the main takeaway for prospective applicants?
This opportunity is best understood as support for research that advances statistical thinking and tool-building, especially work that improves modeling of randomness, learning from data, and decision-making under uncertainty in science and engineering.
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