When we move forward with any hypothesis to test it with statistical analysis, we begin with the assumption that the null hypothesis is correct. There are two ways of making mistakes while dealing with the null hypothesis (Ho) which are type I and type II errors
A type I error happens when the null hypothesis of an experiment is true but rejected often called a false positive.
A type II error occurs when the null hypothesis is false but still not rejected, also known as a false negative.
Type I error is considered to be worse or more dangerous than type II because to reject what is true is more harmful than keeping the data that is not true.