In statistics, type I error is the probability of rejecting a null hypothesis if it is true while type II error is the probability of accepting a null hypothesis if it is false. In real life, you can think of type I error as not doing something you should have and type II error doing something you shouldn't have.
Say you'd like a raise at work. Do you ask the boss? The longer you wait to ask, the longer you keep making the low wage...but if you ask too soon, you might not be qualified to receive the raise. The longer you wait, the more you trade away type II error and accumulate more type I error.
Some people have different preferences over these errors. Type II is more explicit, e.g. I shouldn't have punched that metal pole because now my fingers are broken. Type I error is a little harder to see sometimes, e.g. If I got that raise a year ago, I'd have made an extra $2,000 by now. For that reason, most people are more averse to committing type II errors than type I errors.