What is Error?
In the statistics and hypothesis testing, an error refers to the emergence of discrepancies between the result value based on observation or calculation and the actual value or expected value.
The failures may happen in different factors, such as turbulent sampling, unclear implementation, or faulty assumptions. Errors can be of many types, such as
- Measurement Error
- Calculation Error
- Human Error
- Systematic Error
- Random Error
In hypothesis testing, it is often clear which kind of error is the problem, either a Type I error or a Type II one.
Type I and Type II Errors
Type I and Type II Errors are central for hypothesis testing in general, which subsequently impacts various aspects of science including but not limited to statistical analysis. False discovery refers to a Type I error where a true Null Hypothesis is incorrectly rejected. On the other end of the spectrum, Type II errors occur when a true null hypothesis fails to get rejected.
In this article, we will discuss Type I and Type II Errors in detail, including examples and differences.
Table of Content
- Type I and Type II Error in Statistics
- What is Error?
- What is Type I Error (False Positive)?
- What is Type II Error (False Negative)?
- Type I and Type II Errors – Table
- Type I and Type II Errors Examples
- Examples of Type I Error
- Examples of Type II Error
- Factors Affecting Type I and Type II Errors
- How to Minimize Type I and Type II Errors
- Difference between Type I and Type II Errors
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