Relationship between type 1 error and type 2 error?
In statistics, there are two types of errors for hypothesis
tests: Type 1 error and Type 2 error.
Type 1 error is when the null hypothesis is rejected, but
actually true. It is often called alpha. An example of Type 1 error
would be a "false positive" for a disease.
Type 2 error is when the null hypothesis is not rejected, but
actually false. It is often called beta. An example of Type 2 error
would be a "false negative" for a disease.
Type 1 error and Type 2 error have an inverse relationship. The
larger the Type 1 error is, the smaller the Type 2 error is. The
smaller the Type 2 error is, the larger the Type 2 error is.
Type 1 error and Type 2 error both can be reduced if the sample
size is increased.