In the last post. when I talked about Sampling and Estimation, we discussed P-Value in regression analysis and how this should be less than our error threshold α (alpha). We will understand what is this α value and how we get this while understanding the hypothesis testing.
Hypothesis testing is all about coming up with a hypothesis and figure out if should reject or not. The two components we have here are
- Null Hypothesis or H0
- Alternate Hypothesis or H1
- Together the two hypotheses should cover all possible outcomes
- The two hypotheses should be mutually exclusive.
α is the tolerance level or level of accepting the error. so we can say
P-Value or Probability of current outcome <= α [Reject H0]
P-Value or Probability of current outcome > α [Do not Reject H0]
|Reject H0||Do not Reject H0|
|H0 is True||Type 1 Error||OK|
|H0 is False||OK||Type 2 Error|
α is Probability of Type 1 Error.
Let’s take an example, the judiciary system says “innocent till proven guilty”. So consider this as the null hypothesis
H0 Person is innocent (we need to reject this to prove the person is guilty)
H1 Person is guilty
Type 1 Error: Person is innocent but is treated guilty (we target to minimize this)
Type 2 Error: Person is guilty but is treated innocent