If you are a manager, a client, or a colleague of a statistician, you might want to evaluate their performance and skills. But how do you do that without being a statistician yourself? Here are some tips and criteria to help you assess the quality and effectiveness of a statistician's work.
The first thing to consider is whether the statistician has clearly defined the goals and questions of the project. A good statistician should be able to translate the problem or the hypothesis into a testable and measurable question, and identify the relevant data sources and methods. They should also communicate the assumptions, limitations, and uncertainties of their approach, and explain how they will address them.
Um bom profissional que trabalha com dados, deve ter claro a importancia dessa etapa do trabalho. é essencial que o estatístico saiba fazer as perguntas corretas e levantar hipóteses. E essa é uma habilidade que desenvolve se colocando em situa??es que te desafiam, porque adquirindo experiência, as perguntas e o objetivos v?o ficando mais claros a cada projeto.
The next thing to look at is how the statistician handles the data and the methods. A good statistician should be able to perform data cleaning, exploration, and transformation, and apply appropriate statistical techniques and models to answer the question. They should also be able to justify their choices of methods, and check for validity, reliability, and accuracy. They should also be aware of the ethical and legal issues related to data collection, storage, and use.
Un bon(ne) statisticien(ne) doit ma?triser tout ou partie des méthodes qui permettent de parcourir la cha?ne de données (par ses études et/ou ses expériences) : collecte, traitement, analyse, rapport, communication ...
The third thing to examine is how the statistician presents and interprets the results. A good statistician should be able to summarize and visualize the results in a clear and concise way, and highlight the main findings and implications. They should also be able to interpret the results in the context of the question and the data, and acknowledge the limitations and caveats. They should also be able to compare and contrast their results with previous studies or expectations, and suggest possible directions for further research or action.
In my experience, python libraries such as seaborn, matplotlib are really helpful to represent results. For quick checks also Excel can be useful.
In my domain, clear and concise communication of the results is key. Understanding your audience, and meeting them at their level, rather than going on and on about things their audience may not follow is key. Going beyond the assignment and anticipating follow-up questions (and being able to answer them) is a further plus. Doesn't matter how thorough your analysis is, if you can't explain, present, defend, and support it to stakeholders with varying degrees of statistical backgrounds, it's going to get tossed out.
The fourth thing to evaluate is how the statistician communicates and collaborates with others. A good statistician should be able to explain their work to different audiences, such as technical, non-technical, or managerial, and use appropriate language, tone, and format. They should also be able to listen to feedback, questions, and suggestions, and respond in a respectful and constructive way. They should also be able to work well with other statisticians or experts from different fields, and share their knowledge and skills.
The fifth thing to assess is how the statistician learns and improves their skills. A good statistician should be able to keep up with the latest developments and trends in statistics, and learn new tools and techniques. They should also be able to reflect on their work, and identify their strengths and weaknesses. They should also be open to new challenges and opportunities, and seek feedback and guidance from others. They should also be able to demonstrate their achievements and contributions, and showcase their portfolio.
The sixth thing to do is to give feedback and rating to the statistician. A good feedback should be specific, timely, and balanced, and include both positive and negative aspects. It should also be based on the criteria mentioned above, and provide examples and evidence. A good rating should be fair, consistent, and transparent, and reflect the quality and impact of the statistician's work. It should also be aligned with the goals and expectations of the project, and the standards and norms of the field.