INNOVATION   THROUGH   DATA.

As a trainer, I give workshops, courses, and webinars for university students and employees, research teams, or employees in business contexts (e.g. "corporate academy" settings). I provide training in two areas: empirical methods and computer-supported data analysis, as well as innovation methods and prototyping techniques.

​

​​

​

​

 

Can people apply scientific methods and knowledge without fully understanding them?
Yes.

 

Are they likely to apply it wrongly due to lack of information or misunderstandings?
Also yes.

 

Will they realise they made a mistake, e.g. using a statistical test that looks perfectly fine and the result looks great as well, but they should not have used that specific test in the first place?
No. They will likely not notice. 

Or: If they used a generative AI tools like Gemini, ChatGPT, or Claude, and everything looks and sounds perfectly reasonable to them, are they aware that an expert or supervisor might realise that several things do not add up? E.g. Upon a second read-through, the sentences sound correct, but actually reveal a massive flaw due to a lack of understanding by the writer, the depicted graphs are out of context, or some quotes and citations are entirely made up. 
Again, the answer is likely no. 
 

​

And that is why understanding some scientific basics is necessary... and maybe even unavoidable if you want to do a good job in the end. 

​​

​​

​​

As a scientist and trainer, finding the correct level that allows learners to grasp the fundamental scientific or methodological principles without overwhelming or confusing them — or not providing them with abstract knowledge they ultimately do not need — is honestly quite the balancing act! It's hard work to provide such workshops and training because there (usually) is a mismatch between the abstraction, precision, rigour, or scrutiny required in the "academic world" in contrast to the more complex, messy, and nitty-gritty "real-world". Learners usually want to turn knowledge into applied skills in the world they actually live in, which often does not follow the same requirements and expectations as the world of academia. And it shouldn't have to. There is a trade-off, e.g. people who took my courses may have heard me state something along the lines of  "A true statistician might cuss me out for this, but to make this easier for you to understand... ". Yet, some people may still find that to be "blabla" they do not need or want to hear. 

​

That is why I make sure to get to know and understand my target group of learners to find the appropriate level. Not only participants have to learn, I do as well; by preparing properly. This is what allows me to offer hands-on, practical courses that provide you with as much background knowledge as necessary in a way that you will actually understand and find meaningful, while giving you concrete skills in a pragmatic and realistic manner. You will be able to apply what you learn immediately after the training and know what pit-falls to watch out for while doing so.