While understanding the hypothesis, two errors can be quite confusing. These two errors are false negative and false positive. You can also refer to the false-negative error as type II error and false-positive as type I error. While you are learning, you might think these errors have no use and will only waste your time in learning the concepts.
However, considering the real-life advantages of these errors will change the way you think about them. You will find these errors interesting and exciting.
In many cases, data scientists, while collecting the data, make mistakes or misinterpret situations. When you do not have accurate data, your findings will not be true. A single mistake can make your true results false. Therefore, it is critical to understand how precise interpretation of data can bring accuracy to your research.
Today, we will discuss false negative and false positive and how it affects your outcome. You can refer to your outcome as false positive when you consider the false results as true. In other words, it is a false alarm.
The technical term for this false determination is the type I error. The type II error refers to the condition in which your result is true, but you consider it as false. In other words, a false negative outcome is missing some important data, or the model could not recognize the data. Below, we will understand both the terms in detail:
A false positive condition of a hypothesis indicates that you receive a positive result after conducting a test, but in actual the result should be negative. Other than a false positive or false alarm, you can also refer to this condition as a false-positive error. This condition is common in the healthcare industry. Also, you can use this term for the testing process in other industries, such as software testing. Here are some examples for your reference:
- You receive a positive result from a pregnancy test, but you aren’t pregnant.
- You test positive for Down’s syndrome after a prenatal test, but in reality, you do not have the disorder.
- You do not have any cancer, but your tests suggest that you are suffering from the condition.
- Malware software sometimes considers harmless software as a threat.
Receiving a false-positive result for the medical tests can be troublesome. Therefore, researchers are consistently contributing to reducing false-positive errors. That is why they are implementing new methods to identify the reason for the error and ways to create a more sensitive testing process.
False-negative, on the other hand, is the opposite concept. In this condition, you will receive a negative outcome when the result should be positive. For instance, if you are pregnant but your pregnancy test is showing negative results.
A false-negative test result suggests that the condition such as the disorder does not hold, but it actually does. For instance, if a pregnant woman took the pregnancy test and the result indicates that she isn’t pregnant. A false negative can cause a lot of confusion. When she considers herself negative on a pregnancy test, she will not be able to take care of it. This can lead to various health issues.
You can also understand the concept of false negatives by considering the current pandemic situation. For instance, a person showing COVID symptoms took a test, and his results were negative, despite them being positive. Due to this false test result, they will not isolate themselves and spread the virus to others. Therefore, it is essential to take multiple tests and compare the results. In simple words, a type II or false-negative error is a result of an incomplete test. It is not accurate as some factors influencing the results are absent.
To conclude, we can say that a false negative result occurs when you are not suffering from a disease or condition. But your test result is showing you positively. This happens when the method of data collection is not accurate.
Because the test did not consider all the factors affecting the outcome, the result will be false. False negatives can affect a lot of medical tests. For instance, it can impact a pregnancy test, Lyme or tuberculosis tests, Covid-19 test, and drug tests.
To bring accuracy to the testing system, many data scientists are working on an algorithm that can highlight errors in the test. They will first have to identify the actual reason for the system behaving in an inaccurate way. Once they do, they can find a solution and make the testing process more efficient.
Both these testing errors, type I errors and type II errors, are serious. A false positive or type I error occurs by rejecting the true null hypothesis. However, a false negative or type II error occurs by accepting the null hypothesis as false. According to many data scientists, a false positive is a critical condition. However, we believe that both the errors and troublesome and need to be solved.