How can statisticians improve their communication when working in interdisciplinary teams?

A key lesson I've learned as a statistician in interdisciplinary teams is the importance of understanding your audience. When presenting to non-technical groups, I simplify complex statistical terms, using visual aids to make data accessible. For technical peers, I provide detailed analyses and refer to specific methodologies. Before presentations, I assess the audience's background and tailor my approach accordingly. For instance, with a marketing team, I focus on data trends and consumer insights, while with engineers, I delve into data integrity and analysis methods. Adapting communication based on the audience's knowledge and needs ensures that my statistical findings are both understood and actionable.

I'm gonna say I deal with data everyday but I'd classify myself as an aspiring statiscian. One thing I'd say for sure asides from knowing your audience is; it's very vital to be decisive and pick only what is really relevant to share at a particular time. For me, less is always more.

I have found that using an example to motivate the statistical principle you are trying to teach that is relevant to the learner dramatically increases their understanding. Starting with the problem in mind and presenting the statistical concept as the solution to that problem is a very effective way to teach IMO. To do this you have to know and understand your audience.

Según mi experiencia en la comunicación interdisciplinaria, los estadísticos deben adoptar estrategias efectivas para explicar métodos y hallazgos en equipos con diversas especialidades. Esto incluye simplificar la jerga estadística para traducir términos técnicos a un lenguaje comprensible, sin perder precisión. La visualización de datos es clave, permitiendo representar relaciones estadísticas complejas de forma intuitiva. Usar analogías y metáforas facilita la comprensión, comparando conceptos estadísticos con fenómenos conocidos. El diálogo bidireccional, incluyendo retroalimentación y preguntas, es vital para clarificar el discurso estadístico y fomentar una colaboración efectiva.

To say that much of statistics is not intuitive is an understatement, particularly when it comes to understanding why things are true. Unfortunately, this makes it easier to learn the mechanics of applying various analytical tools than it is to know when particular tools are applicable and when they are not. Hence it is more important to steer your audience away from things they think they already know than it is to introduce them to new and more sophisticated tools.

Learn how to tell a story with the data without dwelling on details about the algorithms but the business value that gets delivered. If your audience wants to know technical details they will typically ask for it. Remember that everyone doesn’t have your understanding of the data and the analysis so don’t forget to start with information you “think” everyone knows, because they don’t.

Using a common ground language and terms is a key to ensure delivering the same idea to all team members. Sometimes, we need to elaborate and/or use non-specialist user-friendly words to keep all persons on the same page. Another thing to be considered is ensuring not to change a key principal when changing the language. Sometimes this is treaky in ways you can't imagine!

If you're presenting statistical analyses to a team where you're the only analyst, which isn't uncommon, then persuade to use regular language, visualizations, storytelling, etc.

The rule of thumb for me has always been to explain the figures like you would explain it to a child. Use the simplest words possible. Remove the cap of a statistician and put on that of your audience. If possible, use instances of what they are most familiar with and watch out for their facial appearance while communicating. It says a lot about whether they're following or you may find a simpler way to explain it.

Esse é um dos grandes desafios do profissional técnico. é necessário trabalhar a comunica??o e entender os conhecimentos dos pares da equipe. Porque eu já vi profissionais com muito conhecimento de estatística, serem describilizados por n?o saberem se comunicar e com isso n?o implementarem os seus projetos.

In my experience as a statistician, especially when explaining SARIMA models to my startup team and stakeholders, feedback has been key. I always ask if they understand and encourage questions. This helps me make sure I'm clear. I also give feedback on their responses to see if they really get what I'm saying. This two-way communication is crucial for teamwork and making complex topics clear. Feedback is a vital tool in making sure everyone is on the same page, especially when dealing with technical subjects like statistics.

Research is for the benefit of the those that needs to know of the research findings, policy makers, ministry of health, health practitioners, media and journalists most importantly the public. Dissemination of findings in the language understood by the audience is key. Important part of dissemination is answering ‘then what?’ How are your results implicating public health? What is then to be done and what do they mean to these stakeholders. So ensuring you discuss and get feedback from the team is vital, communicating each step of your analysis ensuring the team is in one accord.

We often work in and with interdisciplinary teams, collaborating with experts from various fields to solve complex problems that require a combination of scientific expertise. For me, here are some key factors we can employ: know the audience, use plain/simple language and define technical terms, emphasize the problem and its context, use effective storytelling and analogies, visualize data effectively, have active listening and feedback, seek opportunities for communication training, engage with different perspectives and disciplines, embrace open-mindedness and humility & continuously seek feedback and improvement.

If your collaboration can be long term and ongoing then you can learn how to communicate effectively and use language that you know they understand. Being open minded and open to constructive feedback and truly seeking to understand before seeking to be understood is key to communication in a multidisciplinary team.

The problem is it’s hard to get a feedback on a technical work specially if you are the only statistician in the room. The only feedback you can seek is to what extent what have been presented make sense to the audience.

One thing I find helpful is that statisticians can enhance communication in interdisciplinary teams by avoiding jargon, using visual aids to convey complex ideas, actively listening to team members, and tailoring explanations to the specific audience's expertise. Clear and concise language, along with fostering open dialogue, promotes better understanding and collaboration across disciplines.

I highly recommend using charts and diagrams supported with proper visual aids to make it easier to understand the processes. From previous experiences, I witnessed the power of theses tools to appropriately deliver messages by statisticians.

Les statisticiens peuvent améliorer leur communication au sein d'une équipe interdisciplinaire en choisissant des outils et canaux adaptés. Utiliser des visualisations claires pour expliquer des concepts complexes, partager des rapports bien structurés et organiser des réunions régulières sont des moyens efficaces. Les plateformes de communication en ligne, comme les chats professionnels ou les outils de gestion de projet, facilitent également la collaboration. Adapter les modes de communication en fonction des besoins spécifiques de l'équipe garantit une transmission d'information efficace et favorise une compréhension mutuelle.

Use collaborative databases, many of which are free. Most are built on the same core programs Back in the 90s these were being sold bundled with software for engineering analsyses. Along came other software. UNIX has this builtin

Elegir las herramientas adecuadas para la comunicación lo es todo. Se debe considerar tanto la herramienta que permita una explicación sencilla y que podamos manejar con comodidad, como también que sea de fácil comprensión para la persona que recibe la información. Pero por sobre todas las cosas que sea conciso y fácil de leer o ver y entender. Si la presentación o informe es demasiado extenso, es difícil mantener el hilo y se termina por no entender el objetivo.

In my experience it’s key to talk at eye-level. Everyone brings smth to the table. There is a lot we (in each respective discipline) don’t know and understand.

Se existe uma equipe multidisciplinar, é porque espera-se que os integrantes contribuam com suas formas diferentes de pensar. Ent?o, quando entrar em um projeto, absorva o máximo de conhecimento sobre aquele assunto com as pessoas, porque pode ser valioso naquele e nos demais projetos que você venha participar.

Les statisticiens peuvent améliorer leur communication au sein d'une équipe interdisciplinaire en adoptant une attitude d'apprentissage continu des autres membres. Cela implique écouter activement pour comprendre les perspectives et les besoins des collègues provenant d'autres domaines. En posant des questions, en sollicitant des commentaires et en étant ouverts aux idées différentes, les statisticiens enrichissent leur compréhension de la problématique globale. Ce processus favorise une communication plus efficace, car elle est ancrée dans une reconnaissance mutuelle des connaissances et des compétences de chacun au sein de l'équipe.

Learning from others is quite powerful, especially through direct face-to-face communication. In the field of research, it is important the people to interact in person because they give the time and effort to explain efficiently and understand the way that a question can be answered and make a concrete planning for not only the analysis but also for the experimental part that can follow. The constant interaction between biologists and statisticians can improve the question, help for better visualization in the analysis, and create better storytelling in the scientific part. Also, both sides can address questions that make them feel confident and can choose the most appropriate methodology.

En mi experiencia, ayuda mucho dar la charla o presentación previamente a un colega o alguien de confianza que sea equiparable a la audiencia futura. De esta manera se puede corregir o mejorar conceptos que no se entienden o que no habíamos considerado.

I would agree with practice and improve; because the team will give you some suggestions which you can use to improve and simplify things, and by the time you do the main presentation, it is more likely to hit home other than if you never did any mocks.

Allow enough time and try very hard to get a time when those least expert will be present. Breakdown goals of the analyses or better yet, the planned goals. Restate the well-known concerns of avoiding reporting a fake discovery or missing a real discovery. Getting input on confounding factors you have not considered before data collection so you can suggest changes in research design is a gift. The newest person might know the least about the topic & the stats, but might have an insightful “lesson learned” to share. ?Look for smiles & nods in explaining any complicated concept. ?Make eye-contact with the non-experts when discussing basics and eye-contact with experts when discussing advanced concepts.

A prática regular envolve a implementa??o de estratégias de comunica??o em diversos contextos, como reuni?es de equipe, apresenta??es e relatórios escritos. A prática n?o apenas afina as habilidades de comunica??o, mas também promove a adaptabilidade, permitindo adaptar sua abordagem a diferentes dinamicas de equipe e preferências individuais. Além disso, manter-se informado sobre os últimos desenvolvimentos em métodos estatísticos e campos interdisciplinares assegura que sua comunica??o permane?a relevante e alinhada aos objetivos em evolu??o da equipe.

This is something you can do with colleagues you're more friendly with and ask over lunch what didn't work about a presentation in the past. They may be more forward than other members of team. It doesn't need to be an entire formal meeting.

La utilización de PowerPoint como herramienta para la comunicación es altamente efectiva. Su función "ensayar con el asesor" proporciona una valiosa retroalimentación, permitiendo perfeccionar la entrega, tono y ritmo de la presentación. Además, las características visuales facilitan la claridad y comprensión de la información, mejorando la comunicación efectiva de ideas complejas. Integrar esta herramienta u otra similar no solo agrega profesionalismo, sino que también contribuye significativamente a la calidad de la comunicación.

Statisticians tend to use a lot of technical terminology when communicating insights, which might be overwhelming. Effective data storytelling, highlighting the areas most relevant to the business using visualizations, would be a great value addition

Analogies and visuals can go a long way with respect to communication. When I've worked with social scientists and doctors on topology-related papers, we've needed the visual communication of math jargon to avoid confusion and misunderstanding. Analogies can go a long way, as well. For instance, when I was in graduate school, hierarchical clustering was standard in genetics but not in psychology. A symphony analogy was helpful in how different subscales were tied together by mathematical "harmonies" found by the algorithm.

It’s very important to think about your communication style and the style of the people that will be receiving the information. Knowing your audience is also key when delivering a piece of information; always questioning if the audience is more or less technical, and in case of uncertainty, assume no knowledge and explain from the most basic concepts, this way you make sure that everyone can follow the narrative and won’t get confused. Clarifying terminology is also essential, as words can have different meanings depending on the context and scenario they are being used.

Learn to be humble and know what you know and don't know, you do know the stats but your colleagues have the domain knowledge that anchors the results and outcomes in the real world. Mutual respect is the key.

The most important aspect of decoding any analytical problem to nontechnical audience is the art of adroit storytelling. Being parsimonious with the technical jargons, showcasing the results along with the business impact is a hallmark of a seasoned analyst at work. #analytics #business #datascience