Data Visualisation – A Great Topic For Building Your Reputation

In today’s short Friday episode, I talk about into a topic close to my heart—one that I’ve repeatedly leveraged to enhance my reputation: data visualization.

Amidst the complexities of statistical analysis, data visualization stands out as a beacon of clarity.

Unlike many other statistical techniques that demand extensive explanation, data visualization speaks for itself. With a simple glance, it effortlessly communicates intricate patterns, trends, and insights.

In this episode, I explore why mastering data visualization is not just beneficial but essential for actively building a sterling reputation in the statistical community.

I also discuss these important key points:

  1. Importance of Data Visualization
    • Clarity amidst complexity
    • Communicating patterns, trends, and insights effortlessly
  2. Advantages Over Other Statistical Techniques
    • Contrast with techniques requiring extensive explanation
    • Active communication through visual representation
  3. Building Reputation Through Data Visualization
    • Leveraging clarity for personal branding
    • Enhancing visibility and recognition within the statistical community
I urge you to take action on the insights. Remember, data visualization isn’t just a tool—it’s a pathway to clearer communication, enhanced reputation, and greater impact in the statistical community. If you’ve found value in today’s discussion, share this episode with friends and colleagues who can benefit from it. Together, let’s empower one another to master data visualization and elevate our collective expertise.


Data Visualisation – A Great Topic For Building Your Reputation 

[00:00:00] Alexander: Welcome to another episode of the Effective Statistician. This is another short Friday episode and today I want to [00:00:10] talk a topic that is close to my heart because I have used it again and again and again to build my reputation. [00:00:20] And that topic is data visualization. Now, what is data visualization? For all the different things that we do as statisticians, [00:00:30] what makes data visualization?

[00:00:33] Alexander: So different and unique compared to all the other things, [00:00:40] you know, we can be experts in real world evidence and how we make bias adjustments and [00:00:50] how we can handle confounders that happen over time and change over time. How we can make robustness analysis, [00:01:00] all that type of thing. We can be super clever in terms of adaptive design and all kind of different [00:01:10] group sequential design things.

[00:01:12] Alexander: We can be super clever in terms of multiplicity analysis and how we [00:01:20] can best get most out of the alphas that we have. Now, the problem and the challenge with all these kind [00:01:30] of different things is the same. They need a lot of explanation. Very often, you first need to even [00:01:40] explain the problem. Multiplicity, for example, well, most people even struggle with understanding what a p value is in the first place. [00:01:50] And let alone what multiplicity is, let alone all the different techniques that exist there to get most of out of [00:02:00] it.

[00:02:00] Alexander: Now, that’s very, very different with data visualization. Data visualization is easy. [00:02:10] You can just show people what is possible. And you can talk them through examples, you can show [00:02:20] them how what they currently get, and what they could get, just as a contrast. Well, at the moment, we always, for example, produce [00:02:30] these plots using SARS, and we just put them in the appendix, and then we have these line and bar charts, and that’s it.[00:02:40] 

[00:02:41] Alexander: What we could do It is something like this, something that is animated, that, for [00:02:50] example, shows how the path of response increase over time for different groups or how [00:03:00] individual patients behave over time how different groups of patients behave over time, how different endpoints. Work together, and [00:03:10] that, you know, over time, quality of life improves as symptoms get better.

[00:03:17] Alexander: Now, all of these kind of [00:03:20] things you can show very, very nicely using data visualizations. And that’s why data visualizations are [00:03:30] a great way to build your reputation.

[00:03:34] Alexander: Now, if you want to become better at data visualization, of [00:03:40] course you need to spend some time learning about it. Most statisticians have no formal [00:03:50] training about data visualization. We all learned, kind of, by experience. working on our jobs, how to do these [00:04:00] kind of different things. We learned about, okay, yeah, a line graph, a bar chart, a couple of other charts.

[00:04:06] Alexander: We learned about these and we learned, [00:04:10] yeah, they should be more clean. And maybe we picked up some rules about how to use colors and a couple of other [00:04:20] things. But we never learned the theory behind that. What are Principles, design principles, [00:04:30] what are data visualization principles that exists and that are tested and tested and confirmed [00:04:40] again and again.

[00:04:42] Alexander: We don’t need to reinvent the wheels here. There are a lot of theories and models [00:04:50] available that we can learn from. We through this can. Make much more conscious decisions about what will [00:05:00] be a good data visualization, what won’t be a good data visualization, how we need to adapt data visualizations to different scenarios.[00:05:10] 

[00:05:11] Alexander: So invest in your skills to become a better data visualization expert. Someone said. [00:05:20] can use these kind of different techniques to make sure that the communication is better. And when you do that, then you [00:05:30] will build a bigger and better reputation for yourself. You will be able to showcase what you can [00:05:40] do much better, even with all the other things that I have just talked about.

[00:05:44] Alexander: Because Very often, you will need to run kind of various [00:05:50] scenarios, for example, to, to, to, to, to, Showcase, okay, in which scenario, under which assumptions will, which multiplicity [00:06:00] testing yields the best results, or what are the different design features that we can have to design a study in terms of making it [00:06:10] adaptive, and what are the consequences under different scenarios.

[00:06:15] Alexander: For many of these different statistical [00:06:20] approaches, you will need data visualizations as well. So invest in them to be seen as someone that [00:06:30] can communicate clearly. It’s probably not news to you that our community, let’s put [00:06:40] it that way, It’s a little bit limited in terms of communication skills, or at least we are perceived to be limited in terms of communication skills.[00:06:50] 

[00:06:50] Alexander: And yes, there are some outliers. Generally, we were not trained at university to be great communicators, unfortunately. [00:07:00] And we are also very often not the, let’s say, most extroverted people. to build it nicely. And that is not a bad [00:07:10] thing. It’s just the things that we need to take into account. So improve your communication skills using data visualization skills.

[00:07:19] Alexander: And that [00:07:20] will make us as a function, you as a person, better known, and you’ll get a better personal branding through that. Or [00:07:30] if you do that as a group, as a department, you will get a better Branding for your department, for your function, and hopefully we overall as a [00:07:40] community. So please do invest in data visualization skills.

[00:07:45] Alexander: I will put some links to various free [00:07:50] and paid things into the show notes. Check out these show notes and you will find a lot of different resources there. Have a [00:08:00] nice weekend, if you’re listening to this on a Friday, and otherwise, have a nice day. Bye.

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