You're facing pushback from stakeholders on statistical findings. How can you win their support?

Clearly communicate the methodology and data sources. Present results visually using charts or graphs. Highlight practical implications and potential benefits. Address concerns proactively and transparently. Provide context by comparing findings to industry benchmarks. Offer to conduct additional analyses if needed. Engage stakeholders in discussions to understand their perspectives. Frame findings in terms of organizational goals and priorities.

Gaining the support of stakeholders when facing resistance to statistical findings is crucial for the success of any data-driven project. Here are some strategies to help you achieve this: ✅ Understand Stakeholder Concerns. ✅ Communicate Clearly and Effectively. ✅ Relate Findings to Business Goals. ✅ Provide Context and Relevance. ✅ Use a Storytelling Approach. ✅ Offer Actionable Insights. ✅ Address Uncertainties and Limitations. ✅ Leverage Influential Advocates. ✅ Conduct Demonstrations or Pilots. ✅ Offer Continuous Engagement. ✅ Educate Stakeholders. ✅ Highlight Success Stories. By employing these strategies, you can build trust, demonstrate the value of your findings, and ultimately gain the support of stakeholders.

Be transparent, truthful, modest in assumptions and avoid exaggerations! Let the data speak! By not forcing the data to align with prejudices and unreasonable presumptions, as well as acknowledging limitations in the research, stakeholders are able to receive your message with minimum reservations.

I think it is important to distinguish between findings of statistical significance and (what I call) those of managerial significance. The latter is part of the former and involves those that can move the needle from an outcome perspective. It is of course tempting to go down the rabbit hole of "I have found a significant driver in all this data mess" but if it does not have the chance to move the needle from an outcome perspective, it may end up making managers lose interest in the current analysis...and then in future endeavors as well. It is important to communicate that you are looking into "what matters" and "by how much". And then showcase the results

Assuming that the analysis is robust and unbiased, assuming that the shareholders understand it and don't have objections (e.g., aspects that have not been considered in the analysis, specific fact patterns and related necessary adjustments), one additional point to bring up in the conversation is the type of data regulatory authorities would have at their disposal for an independent analysis, and what type of results they would get from similar analyses (e.g., during an audit). If the approach and results will be analogous, then it would be important to mention to the stakeholders that even if the current results are not as expected, ignoring them in the decision making process could pose substantial risks ex-post.

In transforming our insurance organization towards agile practices, we face pushback from stakeholders skeptical about data showing increased customer satisfaction. This resistance stems from concerns about change disrupting workflows and questioning data relevance. To win their support, we must empathize with their concerns, provide concrete examples from within the organization, and showcase early wins that demonstrate agile benefits. Engaging in open dialogue, offering training, and focusing on customer-centric outcomes will help ease fears and build trust, fostering collaboration and a smoother transition to agile practices.

1. Before talking to stakeholders make sure the Data quality is measured and documented in terms of a) Accuracy b) Completeness c) Consistency d) Timeliness e) Validity f) Relevance g) Lineage 2) Once the checks from step 1 is done do exploratory data analysis (eg) look for any outliers skewing the data results 3) Leverage visualisation techniques to highlight the area where the stakeholders have problem with 4) Be open to colloborate with the stakeholders and take their feedback and make correction in the data lifecycle for genuine problems

Bottomline is to convey the view that "we are on the same boat getting to a common destination," rather than "your side of the boat is sinking.":-)

Transparency must be first - once you’ve eliminated misconceptions as a cause for pushback you can move on to goals or whatever else may be causing resistance to your findings

It is important to have a clear understanding of the techniques used to instill confidence in decision-makers regarding the choice of methodology. While detailed information about the methodology may not always be requested, it is essential to have a methodological summary at hand as well as comprehensive support material that outlines the methodology in detail, including: - Data Sources - Data Collection Techniques - Findings from Exploratory Data Analysis - Statistical Methods Used - Validated Hypothesis Tests - Statistical Conclusions

Evite Jargão Técnico: Use uma linguagem acessível e evite termos estatísticos complexos que possam alienar as partes interessadas. Explique os conceitos de forma simples, utilizando analogias e exemplos práticos. Foco no Impacto Prático: Conecte as descobertas estatísticas a implicações práticas e decisões de negócios. Mostre como essas descobertas podem afetar positivamente a organização ou o projeto em termos concretos, como economia de custos, aumento de eficiência, ou melhoria da satisfação do cliente.

Transparency builds trust. Share your methodology, data sources, and reasoning openly. When stakeholders understand how you arrived at your conclusions, it reduces suspicion and increases buy-in. Invite questions and be ready to discuss any uncertainties or limitations in your analysis

As important as it is to be clear and transparent about your data, how it was collected and what it potentially indicates, it is equally (sometimes more) important to be clear about what your data does NOT say. I think the frustration that the audience and stakeholders sometimes experience is when studies do not or cannot answer the questions that the audience is asking.

Simplify the Findings: Avoid jargon and present the findings in a way that is easy for non-experts to understand. Use visuals like charts or graphs to illustrate key points. Contextualize the Data: Explain how the statistical findings relate to the broader context of the project, including potential impacts, benefits, and risks.

Recuerdo que muchos papers y estudios han intentado ser replicados y no se han logrado, por ende, eso hace difícil creer los resultados más allá de la narrativa. Es por ello crucial compartir la metodología, data utilizada y la posibilidad de replicar los resultados de manera independiente, hará más creible los resultados.

Facing pushback on statistical findings from stakeholders 😵? Simplify complexity to win their support. Break down the results into clear, digestible insights using visual aids like charts and graphs created with tools like Tableau or Matplotlib. Avoid jargon; instead, explain concepts in relatable terms. Use analogies to make complex ideas more understandable. Present key takeaways in concise bullet points, emphasizing the practical implications of your findings. Highlight how these insights align with the stakeholders' goals and can drive informed decision-making. By making your data accessible and relevant, you can foster understanding and gain the support needed to move forward.

Na universidade, é comum sermos questionados sobre detalhes técnicos ao apresentar análises estatísticas. Já no ambiente corporativo, o foco é nos resultados e nas ações a serem tomadas. Por isso, é essencial traduzir a parte técnica para uma linguagem mais acessível e prática. Para isso, gosto de usar infográficos e diagramas, que facilitam a compreensão e assimilação das informações pelos ouvintes, tornando os dados mais acessíveis e aplicáveis ao mundo real.

I found it useful to involve stakeholders right from the planning stages and well before conducting data analysis. Discuss with stakeholders how you plan to analyze the data and document it through a well-described statistical analysis plan (SAP). Explain the rationale behind proposed statistical methods, parameter definitions, their estimation, and interpretation, and how statistical significance is assessed. Be transparent on data collection and methodologies limitations, but also on data quality, the role of chance, and potential biases in design, data collection, or methodologies. Full transparency before and after analysis, including clear and regular communication, fosters stakeholder alignment and confidence in results findings.

Statistical findings are acceptable only, when it has real business value. Business people don't care about the findings which are being brought to the table using complex statistical algorithms. Instead of using complex algorithms, we should use simple statistics like mean, variance, trend chart, correlation etc. with proper visualisation of the data. A simple dashboard with simple statistics would be more engaging and embraced by the stakeholders. They use these simple statistics in their day-to-day activities without any resistance.

The focus of evaluation is contingent upon the segment of stakeholders engaged in the process. For an academic audience, the primary considerations include the reliability and validity of the data, as well as the robustness of the dataset itself. In contrast, from a pragmatic professional perspective, the emphasis shifts toward the insights that the dataset can provide in terms of actionable information. This involves assessing the relevance and applicability of the information, as well as evaluating its feasibility concerning factors such as cost, risk, and ease of implementation.

To win stakeholder support, simplify complex data with clear visuals and link findings to business goals. Be transparent about your methods, tell a compelling story, and invite questions to foster trust and understanding.

One of the most important lessons my boss has taught me is that "someone who can explain something in a simple and concise manner to any type of audience is someone who truly knows the subject." It is crucial to be able to simplify complexity and translate it into business language to achieve impact.

Simplify and state only what is necessary. Extra 'floof' just gets in the way and clouds their decision-making ability. Be direct. Be honest. Be genuine. And never answer with "I don't know." Always answer with, "Let me get that answer for you."

It can also be done by simplifying the data through presenting the findings in a clear and accessible way. Make proper use of the visual aids like charts and graphs to make complex data more understandable.

una de las reglas del periodismo es explicar la verdad de tal manera que la persona más baseca educacionalmente pueda entenderla, convertirse entraductor de lo que uno a logrado investigar, aveces pensamos que quienes nos han pedido el trabajo, manejan nuestro idioma y no es así: son personas que necesitan datos pero explicaciones simples y faciles para decisiones agiles y efectivas

When I work with stakeholders, I always fall back on a key concept we experts often forget: Statistics is plural. I would not trust me either if all was based on a single statistic -- or if each statistics was presented as a singular outcome. Instead, be a strategic thinker -- i.e., a "storyteller". This is a point of emphasis in Operations Research. Avoid making immediate conclusions. Instead, begin with what the problem context that they are already familiar with and then build up a story by weaving together your statistical insights en route to your conclusions. In my experience, often even the most resistant stakeholders reach my statistics-supported conclusions long before I actually present them.

It is important to listen and try to understand the reasons behind doubts about a statistical finding. Dialogue is key; in my experience, it is crucial to have preliminary reviews with the decision-makers' teams because they will provide perspectives aligned with the thinking or vision of their director or decision-maker. This facilitates tasks and helps to anticipate and avoid rejection of the findings.

If you're facing pushback, it may be because you haven't yet established trust with the team of stakeholders. Trust is vital and can only be built over time. So, my advice is to do good work for those you are serving statistically, and this will lead to less pushback over time.

In my experience, it is often not the statistical analysis that the stakeholders are actually refuting (unless there was some element of massaging in bad faith)! More often than not, it is because the implications of the findings could be harmful to them in some way. Dialogue helps in teasing that out - is their organization negatively impacted by the findings? Is there some political consideration? Figuring out how to mitigate those and work with stakeholders will have a greater impact than repeatedly pushing the numbers on them.

Bring in other voices as needed. Eg, stakeholders in one area may need to hear from those in another area. Encourage them to think through all elements.

Working with decision makers and statistics often requires to bridge the distribution or potential outcomes vs closed form solutions. Obviously taking a decision on closed form solutions is easier than understanding the outcome space and uncertainty around it.

Engage stakeholders in conversation rather than presenting findings as a final verdict. Ask for their perspectives, address concerns, and be open to feedback. This two-way communication fosters a collaborative environment where stakeholders feel heard and valued, increasing their willingness to support the findings.

Los diálogos con las partes involucradas e interesadas puede ser muy necesario en caso de resultados adversos. Saber transmitir la información sin afectar y de manera no sesgada puede ayudar a disminuir las trabas en la aceptación.

It's important to identify WHY there is pushback in the first place. Your next move depends on this: 1. They don't understand the analyses = simplify it. 2. They don't like the results = they can face this problem now or later, it's their choice. 3. They don't trust the results = let the analyst of their choice take a crack at it (if possible) or find an advocate 4. They mistrust data = relate success stories.

Offering reassurance to stakeholders sometimes needs to be leveled-up. This is often the case when the results of the statistical analysis are different than expected or hoped for. In such instances, conducting sensitivity analyses can be very helpful.

Pushback often stems from fear of the unknown or potential negative outcomes. Reassure stakeholders by highlighting the thoroughness of your analysis and any risk mitigation strategies. Show that you’ve considered their concerns and have plans in place to address potential issues, making them feel more secure in backing your findings.

Use Visualizações Eficazes: Gráficos, infográficos e dashboards interativos podem ajudar a tornar os dados mais compreensíveis e convincentes. Visualizações bem projetadas permitem que as partes interessadas vejam padrões e tendências que podem não ser evidentes em tabelas de dados. Comparações Diretas: Mostrar comparações diretas, como antes e depois, ou diferentes cenários baseados nas descobertas, pode ajudar a ilustrar o valor das conclusões estatísticas.

Preemptively Address Objections: Anticipate potential criticisms and address them directly with evidence. This might involve running additional analyses or explaining why certain decisions were made. Provide Scenarios: Offer different scenarios or sensitivity analyses to show how changes in assumptions affect the outcomes. This can help stakeholders see the robustness of your findings under different conditions.

Para tranquilizar a los observadores del estudio siempre es bueno poner a disposición la metodología utilizada para poder llegar a los resultados, y sustentar con papers o estudios oficiales y académicos para que puedas transmitir seriedad.

Sometimes, the best way to win support is to offer options. Present alternative approaches or solutions based on the data. This empowers stakeholders to make informed decisions, rather than feeling forced into a single path. By giving them a sense of control, you’re more likely to gain their trust and approval.

When addressing an audience with very limited statistical skills, it can be helpful to use visualizations that may seem less intuitive and usefull to an experienced person. Data representation can be paired with a graphical representation of statistical results, making the concept more concrete and tangible for the audience.

es importante tratar de entender el origen de las dudas que se generan en la presentacion pueden venir de malas experiencias de los clientes, es util para saber los flancos a cubrir y que estudios recomendar