Regarded as “false positive and negative”, Type I and Type II Errors are described as statistically impossible to eliminate. The 2 contexts are caused or modified due to the Null Hypothesis (H0). In the case of Type I, the true Null Hypothesis will be rejected, even though the interpretation is right. On the other hand, Type II errors are not rejected, but the hypothesis alternate to the conclusion would be true. Though both the errors might sound confusing, understanding the features and differences will be useful in fields and research studies on Mathematics, Statistical Analysis, Psychology, Medicine, more.
What is A Type I Error?
When a particular condition is deemed as a false hit or absent, then this is said to be the Type I Error. As we read at the beginning, Type I Error will be rejected, even if the Null Hypothesis (H0) for that given condition is true. A few more information about Type I Error.
Type I Errors are called “False Positive” since the condition will express that a constraint is present, even though it is not.
Another name for Type I Error is Alpha level since the symbol that denotes this form of error is the Greek letter α (alpha).
To know how much is the True Null Hypothesis getting rejected is evaluated as rate level or significance level probability.
Inaccurate rejection of a correct Null Hypothesis is frequently noted to be around 0.05 which is 5% probability.
Defining the Type II Error
If a Null Hypothesis is unpredictable to conclude, if something is present or absent is said to be a Type II Error. In this error, the non-rejection of the Null Hypothesis (H0) is noticeable. For a bit more understanding, check out the following pointers.
“False Negative” symbolizes Type II Error because the presence of a real hit is left unidentified and is mentioned as absent.
Here, a True Alternative Hypothesis remains non-supported on any terms.
Denoted with the Greek letter β (beta) and is evaluated using the ‘power of test’ which is equal to 1−β.
Type II errors can be classified with 2 aspects: Either the subject has missed attending to the situation or the subject has misjudged something to something else.
As we now understand what Type I and Type II Errors mean, let’s proceed to understand the outcomes of both. A table given below represents the same in simple terms.
The interpretation or outcome of both the errors (Type I and Type II) are tabulated by understanding the link between the Null Hypothesis to the former and the latter.
Associating the Outcome of Type I and Type II Errors
It would be effective to remember both the forms of errors using real-life case scenarios. Presenting before you 2 easy-to-understand example concepts for both the cases.
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Easy Example Conditions for Type I and Type II Errors
The Medical case of an infant having or not having Down Syndrome and Diagnosed or not Diagnosed for the same.
Hypothesis: An infant is diagnosed with Down Syndrome.
Null Hypothesis: An infant is not diagnosed for Down Syndrome.
False Positive - Type I Error: The infant is not affected by Down Syndrome but is diagnosed.
False Negative - Type II Error: The infant is affected by Down Syndrome but is not diagnosed.
The Courtroom Decision for whether a person is condemned or witnessed as guilty or not, for committing any crime or not.
Hypothesis: The man is condemned.
Null Hypothesis: The man is not condemned.
False Positive: The man is condemned, but has not committed any crime.
False Negative: The man is condemned as non-guilty and the other guilty man is released.
Conclusion
When a Null Hypothesis is true but rejected, then it is called Type I error. When the Null Hypothesis is present, yet rejected as false, then we refer to it as Type II error and mostly covers for a given subject’s misinterpretation. Type I error is also referred to as ‘False Positive’ and ‘False Negative’ denotes Type II error. The applications of Type I and Type II Errors can be found in domains such as healthcare, law, psychology, computer data analysis, and even in many fields of natural sciences.
FAQs on Type I and Type II Errors
1. Losing a high-paying client’s sale due to your assumption is an example for which type of error?
Losing a high-paying client’s sale due to your assumption is an example of Type I error as the individual did not take account of other factors such as timing, nature of the situation, type of work, deadline, etc.
2. How do I know if I have to reject a hypothesis or not?
Using statistical calculations such as Timeslots, Bar graphs, Pie charts, Scatterplots, and more, you can check if a particular hypothesis has to be rejected or not. Note that your statistical calculation varies across the type of data you have (descriptive, inferential, etc.).
3. What type of error is this statement: “I am Innocent but was sentenced for prison”?
The sentence given is an example for a Type I error since the hypothesis is true yet rejected. This is because the subject is denoted as a present (guilt) even though not (innocent).
4. How to reduce a Type II error?
Either relax the alpha level or increase the overall sample size to reduce Type II error. On the other hand, you may also increase the total level of significance to reduce the impact of Type II error.
5. What is the association between Type I and Type II errors?
Type I and Type II errors are regarded to be inversely proportional to each other. So, decreasing/increasing one will increase/decrease the other. Also, note that the Type II error has an inverse link to the statistical test’s power value.
6. Is there a way to reduce the level of Type I error?
Yes. By making use of the prescriptive testing measures, anybody can effectively reduce the level of Type I error. This is possible since the level of ‘confidence’ is increased as a result of these prescriptive tests.
