When it comes to comparing Amazon's AutoML and open source statistical methods, open source methods come out on top. While AutoML may be easy to use and can quickly train models, it lacks the flexibility and control of open source tools. With open source methods, you can fine-tune your models to your specific needs and goals, and you have access to a wide range of algorithms and techniques to choose from. Additionally, the open source community is constantly developing new methods and techniques, so you can always stay on the cutting edge of statistical analysis.

Furthermore, open source methods are often more cost-effective than commercial solutions like AutoML. While AutoML may seem like a quick and easy way to build machine learning models, the costs can quickly add up, especially for large or complex projects. In contrast, open source tools are typically free to use and can be easily integrated into your existing workflow.

So if you want to take control of your statistical analysis and have access to the latest and greatest methods, open source tools are the way to go. Just remember, with great power comes great responsibility, so be sure to use your newfound statistical prowess wisely.

So if you want to be a statistical powerhouse, you'd better hop on board the AutoML train. Just don't forget your space suit and your grim reaper scythe, because with great power comes great responsibility. And if you're not careful, you might just end up dooming humanity to a future ruled by sentient algorithms. But hey, at least you'll have impressive machine learning models, right?