3/1/2023 0 Comments Type 1 errorThis is because statistical power is inversely connected to the Type II error rate and is affected by the alpha (the Type I error rate). Type I and Type II error rates have an impact on each other. When change is required, Type II errors usually result in the status quo (i.e., interventions remain the same). Making a type I error results in adjustments or interventions that are unneeded, wasting time and other resources. Type II error allows a criminal to wander the streets and commit new crimes. On one hand, the social costs of imprisoning an innocent person and depriving them of their personal freedoms are regarded as practically unbearable in our era. In this particular case, considering that the life of a person is at the stake, both types of errors are extremely harmful. Since it’s impossible to say whether a type I or type II error in ML is worse (because it depends so much on the null hypothesis statement), let’s look at which error is more “costly” and for which I might want to undertake further testing. When a person is found not guilty, although they did not commit the offense. When a person is found guilty even if they did not commit the crime, they are sentenced to prison. The individual is not guilty of the offense. This error is defined as “imprisoning an innocent citizen” while a type II error is defined as “allowing a guilty person to go free.” Null Hypothesis Let’s look at a few examples and use a simple form to assist us to grasp the implications and how to find type 1 and type 2 errors. This means that rejecting the null hypothesis mistakenly has a 2% of probability for type 1 error. The alpha, for example, can be reduced to 2%. The alpha can be adjusted because it is determined by the researcher. Minimizing the alpha of a hypothesis test is among the most popular ways to reduce the likelihood of a false positive mistake. There are, however, ways to reduce the chances of getting error results. In hypothesis testing, it is impossible to totally exclude the possibility of type I. How to reduce type 1 error or how to control type 1 error? When we fail to believe a true condition, we commit a Type II error. A type II error, often known as a false negative, occurs when a test result shows that a condition has failed when it has not. When the null hypothesis is wrong but not rejected, it is referred to as a type II error. An alpha level of 0.06, for example, indicates that the true null hypothesis has a 6% chance of being rejected. The alpha represents the likelihood of rejecting the genuine null hypothesis incorrectly. The alpha of a hypothesis test determines the likelihood of making an error. It entails claiming that results are statistically significant when they were obtained only by chance or due to unrelated variables
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