What do you do if you're feeling overwhelmed by complex statistical models?

More often than not, a complex statistical model has its basis in a set of simpler ideas. Start by understanding the simple ideas. For example even with simple linear regressions, you start with a set of 6 (or 7 if you count normality) assumptions. More complex models deal with what happens when these assumptions don't hold. However, if you don't know what these assumptions' value was in the first place, their violation also would not mean much to you although it may have significant negative effects on your analysis.

When overwhelmed by complex statistical models, break down concepts into smaller parts, seek clarification from experts or resources, practice with simpler examples, take breaks to avoid burnout, and gradually increase understanding through persistence and patience.

- Break the model into smaller parts to comprehend each one individually. - Ask for advice and clarification from coworkers, mentors, or online communities. -To improve your understanding, utilize tutorials, online courses, and textbooks. - Take pauses to prevent burnout and give ideas time to settle. - To improve skills, apply the model to various datasets through practice. -Give less attention to theoretical ideas and more to the interpretation and practical application of findings. -To get personalized help and support, speak to a statistician or an expert. -Choose a growth mindset and strive to become an expert in intricate statistical models with perseverance.

No matter how well we understand a problem, there will always come a time when we are stumped- a time when we have no idea where to even begin. While this can be scary, breaking a complex problem into pieces can make it seem less overwhelming which can clear brain space for problem solving. For example, when I was overwhelmed with a homework problem during undergrad I would break it into what I knew and what I didn’t. Clearly knowing the information I lacked helped me find potentially helpful equations which led to potentially helpful variables which led to a potential solution.

1. Break complex and large models into smaller chunks. 2.Visualise the data to understand the intricate patterns inside it. 3.Revisit the fundamentals,sometimes due to stress we tend to forget the very basic and easy concepts. 4.Seek guidance from stakeholders,mentors,colleagues. 5.Observe the batch size and its impact on model performance. 6.Make sure you do the preprocessing steps correctly. 7.Last but not least practice patience, things will fall into place eventually.

It is very common for models to appear more complex than they actually are when we lack knowledge of their construction from more basic principles. Sometimes a model is indeed very sophisticated and complex, but having a solid foundation in probability and in the premises of modeling are things that accelerate the understanding of any model, because the common point that all statistical models have is the foundation in probability and inference.

Remember that a good model is one that performs best while being as simple as possible; follow the principle of parsimony. If you are working with a model and want to achieve better results, try to exhaust all possible variations before moving on to a more complex model. For example, try other variable selection strategies, check if your input data are well-processed and representative, etc.