The Effective Statistician - in association with PSI

Interview with Alun Bedding Understand Business Operations Leadership Is About Inspiring People You Can be a Leader Without a Title Collect Stories and Role Models Passion and Focus Listen to this episode now and share this with your friends and colleagues!

Discussion with Alexander Perfectionism can lead to career failure due to a fear of judgement, or low self-esteem. Procrastination can be caused by an unclear next step, or a task taking too long. Being too busy often leads to stress and an inability to invest in personal development. Fear of failure or rejection can prevent exposure and impede career progression. Missing inspiration or passion reduces motivation and the ability to influence others. By recognizing these five reasons for career failure – perfectionism, procrastination, being too busy, fear of failure or rejection, and lack of inspiration or passion – you will be better equipped to tackle them head-on and achieve your goals. Remember, a successful career is not just about hard work; it’s also about self-awareness, personal growth, and a willingness to take risks. Share this link with your friends and colleagues who can learn from this!

Discussion with Alexander The Academy offers a range of training courses in four distinct areas: Leadership, Innovation, Knowledge and Excellence. Courses are taught by experts in their respective fields and cover topics such as Estimands, Simulation, Data Visualization and Digital Health Applications. Training is both affordable and easily accessible with content split into digestible chunks for maximum convenience. Listen to the episode now to learn more about the conference and share this link with your friends and colleagues!

Discussion with Alexander Schacht What are the key elements to building trust with coworkers and supervisors as a statistician What role does setting expectations and abiding by specific timelines play in the success of a project as a statistician? How can the use of creative data visualizations such as animated scatter plots offer a different perspective on data beyond traditional line graphs and bar charts as a statistician? Some experiences where data visualizations are demonstrated to an advisory board and evoked a "wow" effect for their surprising quality as a statistician Why is data visualization especially effective in communicating complex ideas in a manner that non-statisticians can understand as a statistician? How can investing in data visuals help one stand out and over-deliver expectations in certain cases, potentially leading to promotions as a statistician? Details to provide about your interactive workshops on data visualization that you present both online and in person as a statistician?

Discussion with Alexander and Benjamin How can statisticians, programmers, and data managers work together to develop quality products quickly? What measures can we take to ensure that everyone involved has an understanding of the project's goals and processes? How we can strike a balance between rigorous processes and trusting individuals to make decisions without cutting corners? What strategies we can implement in order to ensure effective communication between all parties involved in a project? In the event of mistakes or setbacks, how can postmortems and lessons learned meetings help to resolve issues with lasting solutions rather than pointing fingers? Listen to this episode and share with your friends and colleagues!

Interview with Miguel Pereira What can statisticians and data scientists learn by exploring the early development research questions? Are there any unique design challenges that they should be aware of? How can Bayesian approaches offer new insights that traditional methods cannot provide? What strategies should data scientists employ in order to successfully navigate the complexities of these new approaches? What knowledge gaps exist regarding the implementation of these new analytical techniques, and how can statisticians and data scientists bridge them in order to maximize their effectiveness? These are all important questions for statistical and data science practitioners, and exploring them further could reveal opportunities for novel ways to approach problems in an increasingly complex field. So, share this link with your colleagues!

Discussion with Alexander We also discuss the following points: How does data visualization require consideration of the needs of an audience? What elements we should take into account when designing data visualizations? Why are standard data visualizations often difficult to create? Why do senior management teams not invest time in understanding the importance of visualizations? How can workshops help senior management better understand data visualizations? What percentage of data visualizations typically has the most impact? How should we allocate resources to ensure maximum impact of data visualizations? Listen to this episode and share this with your friends and colleagues!

Discussion with Alexander and Benjamin We also talk about alternative approaches and some rare cases, where single arm studies might be helpful.

Interview with Shirley Wang The story behind RCT DUPLICATE and how it was developed to facilitate research The approaches taken in conducting these studies. Predictions on how future advancements will affect the development of new therapies. Listen to this episode and share this with your friends and colleagues!

Interview with Rachel Tham What is deep work When have you stepped over this concept of deep work? Which aspects of deep work have helped you most? Were there any guidances in deep work, that you found difficult to follow? How do you use deep work concepts currently in your day-to-day work? References: Deep Work Atomic Habits