Topic 4 DQ 1

Patricia,

Topic 4 DQ 1

A Type I error happens when we reject a true null hypothesis and a Type II error happen when we fail to reject a false null hypothesis. When we decide to reject the null hypothesis, we can be correct or incorrect. The incorrect decision is to reject a true null hypothesis and doing this results in a Type I error.  The text clearly explains that with each test we make, there is always some probability that our decision is a Type I error. Furthermore, the text goes into explaining that a researcher who makes this error decides to reject previous notions of truth that are in fact true. Which means is this is used in a courtroom analogy, making this type of error is the same as finding an innocent person guilty. In order to try and avoid or minimize this error, we need to place the burden on the researcher to demonstrate evidence that the null hypothesis is indeed false (Privitera, 2015).

Privitera, G. (2015) Statistics for the behavioral sciences (2nd ed.). Los Angeles, CA: SAGE. ISBN-13: 9781483381169

Shannon,

Topic 4 DQ 1

A Type I error, is when a researcher rejects a null hypothesis, which can be either a correct or incorrect answer (Privitera, 2015). The researcher that makes this error will reject previous truths that are true.  To minimize this type of error, the burden is placed on the researcher to show that the null hypothesis is false (Privitera, 2015). The book gives an example of finding an innocent man guilty (Privitera, 2015).

A Type II error, also known as a beta error, is an error that occurs in hypothesis testing in which the probability of retaining the null hypothesis is false (Privitera, 2015). Every test a researcher does, there is always a possibility that there will be a Type II error.  When decided to keep a Type II error, the researchers has decided that all the previous statements that were thought to be true, were actually false (Privitera, 2015).  Even though it was an error, the researcher is considered to not have done anything wrong and the researcher can always elect to go back and do further studies.   These types of results are rarely published in scientific journals (Privitera, 2015).

Whitney

Topic 4 DQ 1

Type 1 error is the chance of rejecting a null hypothesis that is true, the reearchers are in direct control for this probablity of commiting this error. A type II error is the chance of retaining a false null hypothesis.  You may avoid creating a type I error by placing the burden on the reasearcher for them to demonstrate eveidening that the null hypothesis is actually false (Privitera, 2015).  The null hypothesis is usally considered to be true so the control for Type I error is by stating a level of significance, this is called the alpha level and this is the largest probablity off committing this type I error.

Glenda

Topic 4 DQ 1

Type 1 error is the reject the null hypothesis and it is true.  Type II error is made when fail to reject the null hypothesis and it is false.  Type I and Type II are related by if one increases the other decreases.  A Type I error is committed when we reject a null hypothesis that is true (Privitera, 2015).  The alpha is the significance level which is the probability of committing the type I error. In the area of distribution curve the points falling in the 5% area are rejected, thus greater the rejection area the greater are the chances that points will fall out of a population in this rejection area and thus more probability of incorrectly identifying true samples in the rejection area. If level of significance reduces from 5 to 1% than the rejection area also reduces thus lower rejection area reduces the chances that points will fall out of a population in this rejection area and thus less the probability of incorrectly identifying true samples in the rejection area. Thus the chances of committing the type I error decreases with reduction in the significance level alpha (Type I and Type II errors, n.d.).