In statistics while testing a hypothesis, you are bound to make type I error (denoted by alpha) or type II error (denoted by beta). Both are inversely proportional to each other i.e. if you try to reduce one type of error, you increase the other type of error.
Type I occurs when you reject null hypothesis when it is true i.e. a blood test will show that you have disease when in fact you don’t. Type II is opposite, here blood test will not show disease, when in fact you have one.
Many found this confusing . I found an interesting fable which helps you to remember the difference between two types of errors.
A beautiful maiden married a handsome prince called Alpha. But after marriage she found that he was impotent. So she got into relationship with Alpha’s ugly looking brother Beta. So for subjects of kingdom there was relationship between Alpha and maiden, when in fact there was none, while according to subjects there was no relationship between Beta and maiden, when in fact there was one.
The King was also a statistician forgave maiden for Type I & Type II error.