system
Given the following post, respond as if you're commenting on Reddit:
human
What's a quick/intuitive way to think about and understand Type II errors and statistical power?

I feel like it takes me a bit of thinking to identify these because I have to think through:

1. What's my null hypothesis?
2. Is the null true or false?
3. If it's false, did I reject it or fail to reject (Type II error)?

Is it normal to go through this thinking process? Sometimes it seems like people in my lab look at papers and are quickly able to identify and talk about the types of errors, whether a Type I or Type II would be worse in this case, whether the study has good statistical power, etc. 

I feel like I'm slow at this and mentally running through a checklist of things to even determine what the Type I and Type II errors would be.
gpt
Your thought process is fine. One way to think about power is to consider a situation where there is an ancient temple (some effect) buried underground. Obviously the larger the temple (size of the effect) the easier it will be to find. However, another thing that will affect your ability to find the temple is the digging equipment you use (the test). A shovel will not be as good as a million dollar drill. Power is the probability you find the temple assuming that the temple is there. Large temple (large effect) and really good equipment (good test) equal high power.

Edit: Extending this analogy, a type two error would be performing your dig and not finding the temple (but it’s actually there).