Representing the Asterisk Nation

The specific problem being addressed is the erasure of Indigenous peoples in statistical representations, leading to their exclusion from policy-making and resource allocation processes. This erasure is pervasive and systemic, resulting in Native Americans being termed an "Asterisk Nation,"  as their population is often too small to be included in typical data analyses. As a consequence, many studies omit Native American data or mark it with an asterisk, indicating a lack of statistical significance.

The exclusion of Native Americans from formal data analyses has significant implications for policy and decision-making. Without accurate representation in research studies, policymakers struggle to justify allocating resources to address the specific needs of indigenous communities, impacting their health and well-being. This issue is particularly prevalent in public health research and governmental surveys, where small sample sizes and administrative hurdles  contribute to the underrepresentation of Native Americans.

Traditional statistical methodologies, such as the frequentist approach, routinely fall short in addressing communities with small populations. These methods lack the capability to discern subtle variations and relationships between variables within limited sample sizes, often leading to studies lacking statistical power and drawing inaccurate conclusions. The only way to avoid these errors caused by underpowered studies is to introduce more information. 

Unfortunately, increasing the sample sizes is impossible for Native American data, as data collection from these small communities is highly expensive and infeasible. Moreover, the practice of aggregating Native American data with other ethnic groups, such as Asian Americans, further muddles critical trends and disparities within indigenous populations, hindering targeted interventions and resource allocation efforts.

This exclusionary practice directly contravenes the justice principle outlined in the Belmont Report.The Belmont Report, issued by the National Commission for the Protection of Human Subjects of Biomedical and Behavioral Research in 1979, outlines ethical principles and guidelines for research involving human subjects. It emphasizes three core principles: respect for persons, beneficence, and justice. Justice underscores the fair distribution of the benefits and burdens of research, ensuring that all individuals and communities have equitable access to the advantages derived from scientific endeavors.The systemic erasure of Native Americans from statistical representations violates the justice principle by denying this population equitable representation in research outcomes and impeding the development of policies tailored to their unique needs. 

By neglecting to report statistics on Native American populations, researchers perpetuate a systemic injustice that hinders progress towards health equity and exacerbates historical disparities. Fostering an inclusive and equitable approach to data reporting is crucial for ensuring that marginalized populations, such as Native Americans, receive the attention and resources they deserve.

Our solution revolves around the implementation of Bayesian statistics by federal agencies when analyzing Indigenous peoples. Bayesian statistics is a powerful approach to data analysis that offers valuable insights, especially when dealing with small sample sizes. Unlike traditional statistical methods that rely solely on observed data, Bayesian statistics incorporates prior knowledge or beliefs about the data before analyzing new information. By integrating existing information with new data, Bayesian methods provide a nuanced understanding of uncertainty and variability within small sample sizes. This flexibility enables researchers to draw meaningful conclusions and make accurate predictions, even in situations where conventional statistical approaches may fall short. In essence, Bayesian statistics empowers analysts to leverage both prior knowledge and observed data to uncover hidden patterns, relationships, and trends, making it a valuable tool for addressing the challenges posed by small sample sizes in data analysis.

Our team’s solution is to develop a comprehensive protocol that offers a step-by-step guide for federal agencies to integrate Bayesian statistical methods into their analyses. This protocol will serve as a practical tool for agencies encountering sample sizes insufficient for adequate precision using traditional frequentist statistics. It will outline the specific steps required to apply Bayesian techniques effectively, ensuring accuracy and reliability in data representation.

Additionally, we will develop workshops to educate these agencies on the importance of accurately representing Native American data and the utility of Bayesian statistics in achieving this goal. These workshops will provide training sessions, resources, and expert guidance to equip agency personnel with the knowledge and skills necessary to implement Bayesian methods confidently.

The last key aspect of our solution involves advocating for the widespread adoption of Bayesian statistics across federal agencies. Through targeted lobbying efforts, we aim to raise awareness about the benefits of Bayesian approaches and encourage policymakers to prioritize their use in data representation, particularly concerning Native American communities. This advocacy will be essential in driving systemic change and promoting the equitable representation of marginalized populations in research outcomes and policy decisions.

In terms of technology, our solution primarily relies on established statistical software packages such as rstanarm, which facilitate the implementation of Bayesian methods in data analysis. These software tools offer user-friendly interfaces and comprehensive functionalities, making them accessible and practical for agencies with varying levels of statistical expertise.

Overall, our solution aims to empower federal agencies with the knowledge, tools, and resources needed to overcome the challenges of small sample sizes in data representation, particularly concerning Indigenous populations. By promoting the adoption of Bayesian statistics and advocating for equitable data practices, we aspire to foster a more inclusive and accurate approach to policy-making and resource allocation, ultimately benefiting marginalized communities and advancing social justice.

Our solution primarily aims to benefit Indigenous communities in the United States, particularly those whose data and representation have been historically marginalized or excluded from policy-making processes. It has the potential to impact all self-identifying American Indians/Alaskan Natives by addressing the issue of data representation in surveys and studies that typically ask participants to self-identify their race. This inclusivity means that all Indigenous peoples can benefit, not just those who live on reservations or are members of federally recognized tribes.

To understand the needs of these communities, we have engaged in extensive consultation with Indigenous organizations, community leaders, and individuals. We have established connections with organizations such as the National Council of Urban Indian Health (NCUIH), the Urban Indigenous Collective, the Ivy Native Council, and IllumiNATIVE, among others. These engagements have allowed us to gain insights into the challenges faced by Indigenous communities. Importantly, our team lead is also a member of the Navajo Nation. 

Our solution benefits Indigenous communities in several ways. Firstly, it provides a framework for more accurate and inclusive data representation, ensuring that Indigenous populations are not overlooked or misrepresented in research studies and policy-making processes. By advocating for the adoption of Bayesian statistics, which are better equipped to handle small sample sizes and incorporate prior knowledge, we empower Indigenous communities to have a voice in decisions that affect their health, well-being, and resource allocation.

Furthermore, our solution promotes Indigenous sovereignty and self-determination by equipping federal agencies with the tools and knowledge needed to accurately represent Indigenous populations in their data analyses. This allows Indigenous communities to advocate for their specific needs and priorities with evidence-based data, leading to more targeted interventions and resource allocations that address their unique challenges and aspirations.

In developing our solution, we are committed to centering Indigenous voices and perspectives. We actively seek input and feedback from Indigenous community members, leaders, and organizations at every stage of the process, from designing the protocol and workshop to advocating for policy change. By ensuring that our solution is informed by and responsive to the needs and priorities of Indigenous communities, we strive to create meaningful and sustainable impact that benefits these communities for generations to come.

Our team’s deep connections to Indigenous communities, combined with our expertise in statistics, public health, and diversity, equity, and inclusion (DEI) make us uniquely positioned to deliver our solution.

Taylor Francisco, our Team Lead, is a member of the Navajo Nation and has extensive connections to various Indigenous organizations and individuals, including the National Council of Urban Indian Health (NCUIH),  the Urban Indigenous Collective, and  IllumiNATIVE. Taylor ensures that our solution is grounded in the needs and perspectives of Indigenous communities. As a Generation Indigenous Ambassador from 2020 to 2022, Taylor demonstrated a strong commitment to advocating for Indigenous youth and promoting cultural empowerment. Importantly, she is also an alumna of Navajo Preparatory School in New Mexico and was a member of the 2019 Youth Council for NCUIH. 

Our team also includes Jeremy Wahl, who brings expertise in public health and marketing, particularly in working with minority populations. Jeremy has served on boards of non-profit organizations in the public health and education space and has studied the effects of administrative burdens on reducing access to basic needs resources from the federal government. With a background working with the disability population and mental health community, Jeremy's experience enables us to effectively communicate the importance of our solution and engage stakeholders in the public health sector. As an estimated 20% of Native Americans face mental illness, Jeremy’s expertise is vital to achieving comprehensive representation of indigenous experiences and struggles. 

Additionally, Sonia Reese, our DEI consultant and executive leadership coach, contributes invaluable insights into ensuring that our solution is inclusive and accessible to all communities. Sonia has served on the boards of numerous non-profits and educational institutions and, with over 30 years of executive leadership of a non-profit aimed at connecting people in economic distress with resources needed to achieve social mobility, she has a proven record of effecting change in relevant fields. With a robust network of donors and advisors across the nation, Sonia helps us navigate complex issues related to diversity and equity, ensuring that our solution meets the highest standards of inclusivity.

Our affiliation with Columbia University, where our team lead earned her Bachelor’s and Master’s degrees, offers invaluable resources in Bayesian statistics. With access to a renowned statistical research community and experts in Bayesian inference, like Dr. Gregory Wawro and Dr. Benjamin Goodrich, we stay updated on cutting-edge methodologies. This expertise ensures our statistical protocol's technical soundness in addressing the challenges of representing small populations in data analysis.

In designing and implementing our solution, we prioritize community input and engagement. We consult with Indigenous organizations, leaders, and community members to understand their needs. Overall, our team's diverse backgrounds, expertise, and connections enable us to design and deliver a solution that is well-informed, inclusive, and responsive to the needs of Indigenous communities. We are committed to ensuring that our solution promotes equity, empowers communities, and drives positive change.

The stage selected for our solution is “Concept”, as it is currently being explored and researched for its feasibility in building a statistical protocol for use by federal agencies. Specifically, the team lead, Taylor Francisco’s Master’s thesis titled "Representing the Asterisk Nation: A Bayesian Approach to Model Trends in Small Populations" has been a foundational step in assessing the feasibility, utility, and validity of utilizing Bayesian inference to represent small populations, with a focus on Native American communities.

Throughout the development of this project, Taylor has conducted extensive research into the challenges faced by Native American communities in statistical representations and data-driven decision-making processes. Drawing from existing literature, case studies, and expert opinions, she has identified the systemic exclusion of Native Americans in traditional statistical analyses as a significant barrier to equitable representation and informed policy-making.

Building upon this research, Taylor proposed Bayesian statistics as a novel approach to address the limitations of traditional statistical methods, particularly in contexts with small sample sizes and heterogeneous populations. By integrating prior knowledge with new data, Bayesian statistics offer a more nuanced understanding of uncertainty and enable more meaningful interpretations and decisions, especially for marginalized populations like Native Americans.

In terms of testing, Taylor’s thesis involved conducting statistical analyses using Bayesian methods to model trends in small populations, using a case study of substance use dependence in Native Americans. This research has demonstrated the feasibility and effectiveness of Bayesian inference in addressing the unique challenges posed by small sample sizes. 

While the thesis project has not yet served a large number of beneficiaries in the traditional sense, its impact lies in its potential to inform and influence policy-makers, researchers, and community advocates working to address health disparities, social injustices, and economic inequalities faced by Native American communities. By demonstrating the feasibility and utility of Bayesian inference in modeling trends in small populations, Taylor’s research has laid the groundwork for future initiatives aimed at promoting equity, justice, and self-determination for Indigenous peoples.

We are applying to Solve because we believe in the transformative potential of our solution to address the erasure of Native Americans in statistical representations. Specifically, our  solution faces several barriers that Solve can help overcome. 

Firstly, legal and regulatory considerations, including data privacy and consent issues, must be addressed when implementing Bayesian statistics within governmental agencies. Solve can provide crucial legal assistance and guidance to navigate these complexities and ensure compliance with relevant laws and regulations. Specifically, determining the extent to which the systemic exclusion of Indigenous peoples in data analysis violates the justice pillar of the Belmont Report would be instrumental in establishing a pathway to mandate the use of Bayesian statistics for this population.

Secondly, there may be market barriers related to the adoption of Bayesian statistics within governmental agencies and the broader research community. Solve's network and expertise can help facilitate lobbying efforts and advocacy campaigns to promote policy changes and foster a more inclusive and equitable data ecosystem.

Lastly, there are financial barriers associated with developing and implementing the protocol. Securing funding for initial development and ongoing maintenance is crucial. Solve's network can connect us with potential donors, investors, or partners who can provide financial support to sustain the project.

Solve can help us overcome the financial, legal, and market barriers that stand in the way of implementing Bayesian statistics and advocating for more inclusive and equitable data practices within governmental agencies.

Our Team Lead, Taylor Francisco, has deep connections to the Indigenous community, particularly within the Navajo Nation, as she herself is a member of the Navajo Nation. These connections extend to various individuals and organizations such as the National Council of Urban Indian Health (NCUIH), Urban Indigenous Collective, Ivy Native Council (and alumni), and IllumiNATIVE, among others. Additionally, Taylor has connections with influential figures such as a former Navajo Nation Police Chief and various members of the Navajo Nation government, which provides her with valuable political contacts. 

Taylor's affiliations with organizations and individuals within the Indigenous community serve as a strong foundation for her project. These connections not only provide her with insights into the specific needs and challenges faced by Indigenous communities but also offer a network of support and collaboration. When developing the protocol for Bayesian inference tailored to the Native American population, Taylor can consult with these networks to ensure that the solution is culturally sensitive, inclusive, and responsive to the community's needs.

Moreover, Taylor's involvement with organizations such as the Urban Indigenous Collective and IllumiNATIVE demonstrates her commitment to advancing Indigenous causes and advocating for Indigenous representation and rights. This alignment with community-driven initiatives underscores her dedication to ensuring that her project has a positive impact on the lives of Indigenous people.

In addition to consulting these networks during the development phase, Taylor can leverage her connections to facilitate outreach and engagement efforts within the community. By actively involving community members in the project's design and implementation, Taylor can ensure that the solution reflects their voices and priorities, fostering a sense of ownership and empowerment among Indigenous stakeholders.

Overall, Taylor's strong connections to the Indigenous community, coupled with her commitment to amplifying Indigenous voices and perspectives, position her as a trusted and effective leader in driving positive change through her project. Through collaboration, consultation, and community engagement, Taylor is well-equipped to address the erasure of Native Americans in statistical representations and promote more inclusive and equitable data practices.

The innovation of this solution lies in its approach to addressing the systemic exclusion of Native Americans in statistical representations and data-driven decision-making processes. While previous efforts have focused on raising awareness of the issue or advocating for better data collection practices, this solution takes a novel approach by advocating for the adoption of Bayesian statistics in governmental agencies for reporting race-related analyses. Additionally, it provides guidance and support for the implementation of Bayesian statistics, particularly in contexts where frequentist methods fail to adequately represent small populations like Native Americans.

Our solution emphasizes community engagement and empowerment throughout the development process. By actively involving Native American communities in the design, implementation, and evaluation of data-related initiatives, the solution ensures that their voices, perspectives, and priorities are central to the decision-making process. This innovative participatory approach not only strengthens the solution's relevance and applicability but also fosters community ownership, agency, and self-determination in data-related initiatives.

In terms of catalyzing broader positive impacts, this solution has the potential to serve as a model for other communities facing similar challenges in data representation and policy-making. By demonstrating the feasibility and effectiveness of Bayesian statistics in addressing the needs of Native American communities, this solution could also help other marginalized populations like Pacific Islanders, Internally Displaced Peoples, and sexual minorities. Additionally, the guidance and resources provided for implementing Bayesian statistics could facilitate knowledge sharing and capacity building among researchers, policymakers, and community advocates across different sectors and geographic regions.

Moreover, by promoting fair representation and informed decision-making, the solution has the potential to catalyze broader positive impacts in terms of health equity, social justice, and economic development. By ensuring that data-driven policies and interventions accurately reflect the needs and realities of Native American communities, the solution could contribute to reducing health disparities, addressing systemic inequities, and promoting the overall well-being and self-determination of Indigenous peoples.

The theory of change behind our solution lies in the application of Bayesian inference to address the systematic exclusion of Native Americans in statistical representations, particularly in policy formulation and funding allocation processes. 

The long-term objective of the program is to achieve improved data representation, policy formulation, and resource allocation for Native American communities, ultimately leading to enhanced social, economic, and health outcomes for Indigenous populations.

The program team, in collaboration with Indigenous organizations and academic experts, is designing a specialized Bayesian protocol tailored to analyze data related to Native American communities. This includes conducting research, gathering existing knowledge, and consulting with stakeholders to inform protocol development.

Program staff will organize workshops and training sessions to educate governmental agencies and researchers on the importance of Bayesian statistics and Indigenous data sovereignty. These educational activities involve developing training materials, delivering workshops, and providing ongoing support to participants.

The program will also engage in advocacy efforts to promote the adoption of Bayesian statistics by governmental agencies. This includes conducting policy briefings, engaging with policymakers, and advocating for policy changes to address the erasure of Native Americans in data representation.

The output of the protocol development activity is a comprehensive Bayesian protocol document outlining guidelines and procedures for analyzing data related to Native American communities. The program will produce educational resources, including workshop materials, training modules, and informational guides, to support the capacity-building efforts of governmental agencies and researchers in implementing Bayesian statistics.

Outputs from the lobbying and advocacy activities include policy briefs, advocacy materials, and engagement strategies aimed at influencing policy changes and promoting the adoption of Bayesian statistics in governmental agencies.

As a result of implementing the Bayesian protocol, there will be improved data representation of Native American communities in statistical analyses, leading to a more accurate understanding of their needs and challenges.

Through educational workshops and advocacy efforts, policymakers and researchers will gain a deeper understanding of the importance of incorporating Indigenous data sovereignty and Bayesian statistics in policy formulation. This will lead to more informed decision-making and targeted policies that address the unique needs of Indigenous populations.

By actively involving Indigenous communities in the process, our solution ensures that their voices are heard and their perspectives are integrated into the protocol and advocacy efforts. This participatory approach fosters trust, ownership, and sustainability within the community.

Overall, the application of Bayesian inference in conjunction with advocacy, education, and community engagement is expected to have a transformative impact on the problem of Native American erasure in statistical representations. By addressing the root causes of exclusion and advocating for more inclusive data practices, our solution aims to promote equity, representation, and informed decision-making for Indigenous communities.

Our three impact goals for our solution are focused on achieving systemic change in the representation and inclusion of Native American communities in data analysis and policy-making processes. These goals are aligned with the broader objective of promoting equity, fairness, and informed decision-making for Indigenous populations.

1. Goal: Increase Representation in Data Analysis

    - Objective: Ensure that Native American populations are accurately represented in statistical analyses conducted by governmental agencies and research institutions.

    - Indicators:

     - Number of governmental agencies adopting the protocol for Bayesian statistics.

     - Percentage increase in the inclusion of Native American data in reports and analyses within these agencies.

     - Feedback from stakeholders on the effectiveness of the protocol in improving representation.

2. Goal: Enhance Policy-Making Processes

    - Objective: Enable policymakers to make more informed decisions by providing robust data and insights on Native American populations.

    - Indicators:

    - Number of policy initiatives or interventions informed by data generated using Bayesian statistics.

    - Impact assessments of policy changes on Native American communities.

    - Stakeholder perceptions of the usefulness and relevance of data-informed policies.

3. Goal: Foster Collaboration and Engagement

    - Objective: Build partnerships and collaborations with Indigenous communities, governmental agencies, and other stakeholders to ensure the sustainability and relevance of the solution.

    - Indicators:

    - Number of partnerships established with Indigenous organizations and community groups.

     - Feedback from stakeholders on the inclusivity and effectiveness of collaboration efforts.

Measuring Progress:

- We will track the adoption of the protocol by governmental agencies through formal agreements, pilot programs, or memoranda of understanding (MOUs).

- Surveys and interviews will be conducted with stakeholders to assess their perceptions of the impact and effectiveness of the protocol in improving data representation and policy outcomes.

- Impact assessments will be conducted to evaluate the effectiveness of data-informed policies in addressing the needs and priorities of Native American communities.

The core technology powering our solution is Bayesian statistics, specifically implemented through established packages in the R programming language, such as rstanarm. Bayesian statistics offers a modern approach to data analysis that aligns with the principles of our solution, which seeks to address the erasure of Native American communities in statistical representations.

Bayesian statistics differs from traditional frequentist methods by incorporating prior knowledge into statistical inference. This prior knowledge can include existing research findings, expert opinions, and, importantly, traditional Indigenous knowledge systems. By leveraging Bayesian statistics, we aim to not only improve the accuracy and precision of data analysis but also to incorporate Indigenous perspectives and methodologies into the process.

One key advantage of utilizing R packages like rstanarm is their accessibility and ease of implementation. These packages provide a user-friendly interface for conducting Bayesian analysis, making it accessible to a wide range of researchers and practitioners, including those with limited statistical expertise. This accessibility is crucial for promoting the widespread adoption of Bayesian statistics in research and policy-making contexts, ultimately leading to more inclusive and equitable outcomes for Indigenous communities.

In addition to leveraging existing technology, our solution will also explore ways to incorporate traditional Indigenous knowledge into Bayesian priors. Traditional Indigenous knowledge encompasses centuries-old practices, beliefs, and insights passed down through generations, which offer valuable perspectives on community health, well-being, and resource management. By integrating traditional Indigenous knowledge into Bayesian priors, we not only honor and respect Indigenous cultures but also enhance the relevance and applicability of statistical analyses to Indigenous communities.

Overall, our solution harnesses the power of Bayesian statistics and established R packages to address the erasure of Native American communities in data representation. By combining modern technology with traditional Indigenous knowledge systems, we aim to create a more inclusive and equitable approach to data analysis and decision-making, ultimately benefiting both people and the planet.

New York City, New York. 

Navajo Reservation in New Mexico and potentially other tribal lands such as Menominee Reservation in Wisconsin. 

There are three unpaid workers who are volunteering their time to develop this statistical protocol (Taylor Francisco, Jeremy Wahl, Sonia Reese). 

Taylor began developing the research for our protocol in 2021. Jeremy and Sonia joined the Auxen Policy Lab team at the beginning of 2024.

Ensuring diversity, equity, and inclusion (DEI) within our team is a fundamental aspect of our approach to addressing the erasure of Native Americans in data. We recognize the importance of fostering a welcoming and inclusive environment where all team members feel valued, respected, and supported.

1. Leadership Diversity: Our team is led by individuals with diverse backgrounds, experiences, and expertise. In particular, Sonia Reese brings extensive DEI expertise from her previous role as the Executive Director of a community service non-profit at Columbia University. Her leadership guides our efforts to prioritize DEI considerations in all aspects of our work.

2. Commitment to DEI Goals: We have established clear goals for increasing diversity, equity, and inclusion within our team. These goals include recruiting team members from diverse backgrounds, promoting equitable opportunities for professional growth and advancement, and creating a culture of inclusion where all voices are heard and valued. In particular, we plan on creating an internship program that will allow indigenous undergraduate students to participate in the development and implementation of our solution. Additionally, we will attend conferences and engage with professional societies such as the Society for Advancing Chicanos/Hispanics and Native Americans in Science (SACNAS). 

3. Open Communication and Feedback: We maintain open lines of communication and encourage feedback from all team members to ensure that everyone's perspectives and concerns are heard and addressed. Regular check-ins, team meetings, and anonymous feedback mechanisms help to create a safe and supportive environment for sharing thoughts and ideas.

4. Community Engagement: We actively engage with diverse communities, including Indigenous groups, to ensure that our work is informed by the needs and priorities of those we aim to serve. By building strong partnerships and relationships with community stakeholders, we ensure that our solutions are culturally sensitive, relevant, and impactful.

5. Continuous Improvement: We are committed to continuous improvement in our DEI efforts. We regularly assess our progress, identify areas for growth and development, and adjust our strategies and practices accordingly to create a more diverse, equitable, and inclusive team environment.

By prioritizing diversity, equity, and inclusion within our team, we not only create a more welcoming and supportive workplace but also enhance the effectiveness and impact of our solutions in addressing the needs of marginalized communities, including Native Americans.

Our business model as a non-profit revolves around providing value to governmental agencies, research institutions, and Indigenous communities by offering expertise in Bayesian statistics and data analysis tailored to address the erasure of Native American communities in statistical representations.

Key Customers and Beneficiaries:

1. Governmental Agencies: We target governmental agencies responsible for policy-making and resource allocation, particularly those involved in public health, social services, and indigenous affairs. These agencies benefit from our expertise in Bayesian statistics to improve the accuracy and equity of data analysis, leading to more informed decision-making and targeted interventions.

2. Research Institutions: Academic and research institutions focused on public health, social sciences, and Indigenous studies are another key customer segment. Our solution provides these institutions with advanced statistical tools and methodologies to enhance the quality and relevance of research outcomes related to Indigenous communities.

3. Indigenous Communities: Ultimately, the Indigenous communities themselves are the primary beneficiaries of our solution. By ensuring their accurate representation in data analysis and decision-making processes, we empower Indigenous communities to advocate for their needs, access resources, and drive positive change within their communities.

Products or Services:

1. Bayesian Statistical Analysis: Our core offering is expertise in Bayesian statistics and data analysis, tailored specifically to address the challenges of representing Native American communities in statistical representations. We provide consultation, training, and support to governmental agencies and research institutions in implementing Bayesian approaches to data analysis.

2. Protocol Development: We develop protocols and guidelines for governmental agencies and research institutions to incorporate Bayesian statistics into their data analysis practices effectively. These protocols serve as practical frameworks for conducting Bayesian analysis and ensure consistency and accuracy across different projects and initiatives.

3. Capacity Building Workshops: We conduct capacity-building workshops and training programs to equip stakeholders with the necessary skills and knowledge in Bayesian statistics. These workshops cater to diverse audiences, including policymakers, researchers, and community leaders, fostering a culture of data literacy and empowerment within Indigenous communities.

Value Proposition:

Our value proposition lies in providing a holistic solution to address the erasure of Native American communities in statistical representations. By leveraging Bayesian statistics and incorporating traditional Indigenous knowledge, we offer a unique approach that enhances the accuracy, equity, and relevance of data analysis. Our services empower governmental agencies, research institutions, and Indigenous communities to make informed decisions, advocate for their needs, and drive positive change.

Our plan for financial sustainability revolves around securing initial funding for the development of the protocol and workshop, with minimal ongoing expenses for maintenance and updates. As a non-profit solution, our focus is on providing valuable services to our target audience without seeking direct revenue generation.

1. Grants and Donations: We aim to secure grants and donations from philanthropic organizations, governmental agencies, and foundations that support initiatives aimed at addressing Indigenous health disparities and promoting data equity. These grants will provide the initial funding needed to develop the protocol, conduct capacity-building workshops, and cover operational expenses.

2. Collaborative Partnerships: We seek to establish collaborative partnerships with governmental agencies, research institutions, and Indigenous organizations to co-develop and implement the protocol and workshop. These partnerships can also involve in-kind contributions such as expertise, resources, and infrastructure, reducing our reliance on external funding sources.

3. Leveraging Existing Resources: We will leverage existing resources and infrastructure available within academic institutions, such as Columbia University, where our team lead is affiliated. This includes access to research facilities, expertise in Bayesian statistics, and institutional support for community engagement initiatives.

4. Sustainable Growth Strategy: As our solution gains traction and demonstrates its impact, we aim to attract additional funding opportunities through competitive grants, awards, and partnerships. We will also explore opportunities for scaling our impact by expanding our services to other marginalized communities and geographic regions facing similar challenges.

Evidence of Success:

While our solution is in the concept stage, we have received positive feedback and interest from potential stakeholders, research institutions, and Indigenous organizations. Our team lead's affiliation with Columbia University provides credibility and access to resources for advancing the solution.

Additionally, we have begun preliminary discussions with potential funding partners and collaborators to explore opportunities for financial support and partnership. These early engagements indicate a promising pathway for securing the necessary funding to develop and implement the protocol and workshop.

Furthermore, our commitment to a non-profit model aligns with the values and goals of many funding organizations focused on social impact and community empowerment. By prioritizing impact over profit, we aim to build a sustainable model that ensures long-term benefits for Indigenous communities and stakeholders.