What are the best ways to communicate with statisticians on your team?

I’ve found that one of the most important elements of analysing data is actually very much in the planning phase. In our world today, it is easy to collect tons of data with just a few clicks. However, for data to be meaningful and valuable, we must first ask ourselves - what do I hope to find out from this data? What questions can elicit these in the most complete and accurate way? How can I do this in a way that is sensitive to data protections, but pack the most punch? Starting with the end in mind really helps.

As a statistician/data scientist who worked with teams of members from diverse disciplines to serve clients of different sectors, I would like to emphasize the importance of genueinly trying to understand the experts of statistics. For instance, if you did not fully understand what the confidence bound translates into real terms or p-value in the report signify what; please ask them. They will be very probably more than happy to help. Because, they really are important concepts rather than small numerical details.

There are a few key things to keep in mind when communicating with statisticians on your team. First, it is important to be clear and concise in your communication. Avoid using jargon or technical terms that may not be familiar to everyone. Second, it is important to make sure that you are both using the same terms to refer to the same concepts. For example, "mean" and "average" can mean different things in different contexts. Third, be respectful of the statisticians' expertise and time. Finally, if you are unsure about something, don't be afraid to ask for clarification.

For a successful partnership with statisticians on your team, keep it clear and straightforward. Begin with well-defined project goals and use plain language when communicating. Adding context by explaining the project's background and engaging statisticians early is key. Ensure the data provided aligns with their analysis needs and ask questions when in doubt. Keep expectations realistic to avoid misunderstandings. Regular updates and feedback sessions help maintain open communication. Trust in their statistical expertise, and maintain thorough records of important decisions for transparency.

First, I will tell my data analyst what interests me. I will also ask her to determine the correlation and causation and check for the root cause. Graphing everything out makes it much easier to understand each other.

Any data scientist or statistician who worked with data from the market would confirm, it is almost impossible to deliver the perfect data in one batch. It is just as it is the things are. Most of the time, you would need to reiterate numerous times with the client to have a data of sufficient quality to work with. Therefore, it is very important at this step both parties to provide clear instructions and demands.

Involve your statistician *early* in the process! They’ll help you assess the soundness of your experiment. If you come to them after collecting the data, do not expect more than an autopsy.

I think one of the more important things is the data cleanliness. Like how much do you trust this batch of data? You gotta talk to your statistician to understand if the experiment has produced rubbish data.

For effective communication with statisticians, it's crucial to offer precise and pertinent data. They rely on accurate information and an understanding of the project's context and goals. Clearly define the problem, scope, constraints, and desired results. Share data sources, definitions, formats, and quality assessments. Specify the required level of detail, accuracy, and confidence. Avoid using vague, ambiguous, or inconsistent terms or requests to ensure statisticians can work efficiently and effectively.

Selecting the right statistical software is like choosing the most effective means of communication for your data needs. R and Python are versatile and widely understood, while IBM's SPSS is user-friendly. SAS is ideal for complex discussions, Excel for straightforward ones, and Tableau for visual storytelling. JMP provides an engaging conversation, MATLAB a mathematical exchange, and STATA is tailored for the social sciences. Your choice should align with the complexity of your analysis and your familiarity with the tool.

It is a statistician's duty to deliver his/her message accurately in a custom package for the intended audience. Though, statistical literacy at its basics is a must, we can not expect everyone to grasp all concepts. Through my career, I observed that one of the most difficult concepts to interpret for non-technical audiance is the ambugouis nature of things and statistical confidence. Even though it seems straightforward you may need to invest some time to explain it.

Graph, graph, graph. Infographics. No one has the time and mood to sit through the rows and columns of numbers or texts. If there is one number that needs to be presented, it should be the correlation coefficient.

Communicate effectively with statisticians by using their technical language and tools. Learn key statistical concepts and terminology, and interpret common statistical results. Employ visual aids, examples, and compatible software or formats to align with their expertise and preferences.

Interactive and accurate feedback loops are essential for statistical works. One of the most vital aspects of it is scientific veritability. A statistician should always deliver his/her results with measurable and verifiable confidence. For that, mastering statistical tests and hypothesis formulation is paramount of importance.

Constructive feedback means honest feedback. If the analysis presented to me is not useful, I will tell it straight. If it has helped me in my work, I will also tell it straight.

To effectively engage with statisticians, offer constructive feedback and support. Acknowledge their contributions, express appreciation for their work, and provide suggestions for improvement. Encourage collaboration, innovation, and skill development. Show a willingness to assist and offer resources to facilitate their growth and effectiveness in their roles.

Effectively communicate with statisticians by establishing realistic expectations and deadlines. Address project complexity, feasibility, and milestones. Monitor progress, communicate changes or issues, and be adaptable to evolving situations and needs, recognizing the challenges statisticians face in their work.

One overlooked aspect of statistics based projects is that usually they require continuous adaptation and improvements. And this might be a little bit more challenging compared to somehow more structured DevOps cycles of other technical disciplines. The challenge comes from the fact that the change is generally induced by evolving data and requirements. I call this StatOps. So, a statistician leader generally needs to adopt a similar kind of an approach.

True. I found that most of their time is spent on data cleaning. This work cannot be automated (yet), and takes experience and judgment. If the data is dirty, the analytics will be bad and not useful. We have to understand that graphing data out only takes a few seconds, but bulk of the work is in arranging the data.

Part II: So where to start? As usual: From your project goals, requirements and underlying specifications. All this drives the necessary involvement in depth and width, by and with experts like statisticians. And yes, in more complex cases, you need to find a common ground and understanding (and “language") first before you start talking about the project requirements. This process in turn requires willingness and openness to work on par, listen, learn from each other.

In order to create an efficient communication channel with your data science and statistics team, it is imperative to be willing to comprehend uttermost important statistical concepts like p-value significance, probability distributions, confidence levels etc. Otherwise, it would be difficult for you to further coordinate the efforts. If you consult your team, very probably they would be glad to help.

Part I: My understanding is that we are talking about statistics and statisticians in the industry (or public sector), not research. If so, as noted in another post already, statisticians are typically part of a cross functional team, and from my experience, the points 1 to 6 addressed here apply more or less similarly to each of the (highly specific, complex, and so on) fields required for a given project, together with their respective "languages", tools and ways of working and thinking.