Lindsay Dodson 7:51pmApr 15 at 7:51pmManage Discussion Entry A “Type I” also known as a false positive can occur when a researcher ultimately rejects the null hypothesis. This is pretty much the...

Lindsay Dodson

A “Type I” also known as a false positive can occur when a researcher ultimately rejects the null hypothesis. This is pretty much the researcher making an inaccurate assumption of the results of the research. The “Type 2” would be when the researcher accepts the null hypothesis. Normally this is due to huge variances in population size, too small sample size, and so on (Cozby & Bates, 2017).

1. Research question #1: What impact does mental health have on job satisfaction?




1. Hypothesis #1 There is a direct correlation between mental health and job satisfaction


2. Type 1 error, the hypothesis is true however, the researcher rejected it. Making the assumption that there is no correlation between the two variables, even though the end result made the hypothesis correct.


3. Hypothesis #2 There is little to no correlation between mental health and job satisfaction.


4. Type 2 error the hypothesis is false, however, the researcher accepted. The researcher made the assumption that there is little to no correlation between mental health and job satisfaction.

1. Research question #2: What types of impact does remote work have to work-life balance?




1. Hypothesis #1 After the one-year mark remote workers are 85% more likely to have a better work/life balance than going into the office every day.


2. Type 1 error: H1 to is true, but the researcher rejected it. The researcher states remote workers are now 85% more likely to have a better work-life balance than when they had to show up to an office 5 days a week.


3. Hypothesis #2 After one year of remote work, remote workers are 85% more likely to not want to go back into an office building for work again.


4. Type 2 error: H2 is false, but the researcher accepts it. The researcher assumed everyone in this category (85%) would not want to go back into an office for work.

Understanding when to accept and reject the null hypothesis is a huge part of testing and researching. As well as making sure you have the right variables and sample sizes. This allows you to valid research, which in the end allows contributions to knowledge for future generations.

References

Cozby, P. & Bates, S. (2017).Methods in behavioral research (13th ed). New York, NY: McGraw-Hill.

Daniel Hoogestraat

Hypothesis Testing

There are four primary steps in data-driven decision-making. First, a researcher must formulate a hypothesis. Second, they must find or develop the right tests. Third, they execute the test. And fourth, they make a decision or conclusion based on the test results (Cozby & Bates, 2017). A hypothesis is a statement or claim about a population parameter (i.e., mean, variance, proportion, etc.) When researchers test their hypothesis, they are evaluating the null hypothesis (population means are equal) and the “assumption” that the population means are not equal (Cozby & Bates, 2017). In other words, in hypothesis testing, the researchers are working to discover whether there are or aren’t differences in performance depending on the effects of an independent variable. Symbolically, the null hypothesis is labeled H0and the assumption or research hypothesis is labeled H1(Cozby & Bates, 2017).

Through the proposed research questions below, I will demonstrate my understanding by identifying the alternative and null hypotheses associated with each proposal. Furthermore, I will discuss how and why type 1 and 2 errors occur and provide examples of both errors for each question.

Type 1 & Type 2 Errors

Generally speaking, a “Type I” (or a false positive) error occurs when a researcher rejects a “true” null hypothesis. Essentially, the declaration of a Type 1 error lies on the researcher’s inaccurate assumption. However, a “Type 2” error occurs when a researcher accepts a “false” null hypothesis. Essentially, the declaration of a Type 2 error lies in inadequate sample size, population variance, or other factors (Cozby & Bates, 2017).

Research Questions, Hypothesis, & Errors: Telework

1. 
   Research Question:To
   what extent is depression and job satisfaction related to isolation in a telework environment?
   
   




• 
  H0–Teleworkers are 25% more susceptible to depression compared to non-teleworkers.




• 
  Type 1 Error:H0is true, however, rejected by the researcher. In other words, the researcher assumed that teleworkers were not 25% more susceptible to depression, and yet testing proved this to be true.




• 
  H1–Teleworkers are ≤ 25% more susceptible to depression compared to non-teleworkers.




• 
  Type 2 Error:H1is false, however, accepted by the researcher. In other words, the researcher assumed that teleworkers are ≤ 25% more susceptible to depression yet testing proved this to be false.






2. 
   Research Question:What impact does telework have on Physical health?
   
   




• 
  H0–After 1 year of Telework, Teleworkers are 15% more likely to suffer from lumbar pain compared to employees who work 1 year in a non-teleworking environment.




• 
  Type 1 Error:H0is true, however, rejected by the researcher. In other words, the researcher assumed that teleworkers were not 15% more likely to suffer from lumbar pain, yet testing proved this to be true.




• 
  H1–After 1 year of Telework, Teleworkers are ≤ 15% more likely to suffer from lumbar pain compared to employees who work 1 year in a non-teleworking environment.




• 
  Type 2 Error:H1is false, however, accepted by the researcher. In other words, the researcher assumed that teleworkers are ≤ 15% more susceptible to depression yet testing proved this to be false.

Concluding Remarks

Assessing whether to accept or deny the null hypothesis is a fundamental construct of hypothesis testing and research. Implementing viable sample sizes and selecting the right tests are crucial to accurate results. Validating research by way of test and procedure is essential because it contributes to the body of knowledge for generalization and future applications.

References

Cozby, P. & Bates, S. (2017).Methods in behavioral research (13th ed). New York, NY: McGraw-Hill.