Type-1 Error
This article covers meaning & overview of Type-1 Error from statistical perspective.
What is Type-1 Error?
Statistical errors are an integral part of hypothesis testing. Type-I Error is the error which is used to reject a true null hypothesis (Ho). It is also known as “Error of the first kind”.
In simple words, Type- I error indicates that a given condition is true, when it is actually false. It means that we believe a falsehood.
The probability of type-I error is denoted by α (alpha). α is also called as the bound on Type I error. It is the level of significance of the test.
FORMULA:
Since, α is a conditional probability, which can be calculated as follows:
α = P(Rejecting H0│H0 is True)
Errors in Hypothesis Testing:
|
Decision |
Ho True |
Ho False |
|
Reject Ho |
Type I Error (α) |
Correct Assessment |
|
Fail to reject Ho |
Correct Assessment |
Type II Error (β) |
Since Type I is the more serious error (usually), that is the one we concentrate on. We usually fix α to be very small (0.05, 0.01).
EXAMPLE:
When a person is accused of a crime, we put him on trial even after knowing his innocence. Type- I error in this case is that the person is truly innocent but the jury finds him guilty.
Hence, this concludes the definition of Type-1 Error along with its overview.
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