Type 1 and Type 2 errors

Type 1 error is a type of error that occurs when there is a rejection of the null hypothesis when it is actually true. This kind of error is also called an error of the first kind and is equivalent to false positives.

Let's understand this concept using an example. There is a new drug that is being developed and it needs to be tested on whether it is effective in combating diseases. The null hypothesis is that it is not effective in combating diseases.

The significance level is kept at 5% so that the null hypothesis can be accepted confidently 95% of the time. However, 5% of the time, we'll accept the rejecttion of the hypothesis although it had to be accepted, which means that even though the drug is ineffective, it is assumed to be effective.

The Type 1 error is controlled by controlling the significance level, which is alpha. Alpha is the highest probability to have a Type 1 error. The lower the alpha, the lower will be the Type 1 error.

The Type 2 error is the kind of error that occurs when we do not reject a null hypothesis that is false. This error is also called the error of the second kind and is equivalent to a false negative.

This kind of error occurs in a drug scenario when the drug is assumed to be ineffective but is actually it is effective.

These errors can be controlled one at a time. If one of the errors is lowered, then the other one increases. It depends on the use case and the problem statement that the analysis is trying to address, and depending on it, the appropriate error should reduce. In the case of this drug scenario, typically, a Type 1 error should be lowered because it is better to ship a drug that is confidently effective.