Assignment:Response to Discussion regarding Type 1 and Type II errors

Assignment:Response to Discussion regarding Type 1 and Type II errors ORDER NOW FOR CUSTOMIZED AND ORIGINAL ESSAY PAPERS ON Assignment:Response to Discussion regarding Type 1 and Type II errors Please provide a brief response discussion and provide reference within 5 years. I will attach the file discussion numbered 1 to 4. Assignment:Response to Discussion regarding Type 1 and Type II errors attachment_1 Number 1 Type I is when the researcher claims the level of efficacy was met when actually it wasn’t. This is the alpha probability of flaws/errors in statistics, according to Houser (2018). This is a serious error in which the researcher claims results to be effective without mitigating the probability of uncontrolled variables. Potentially, this type of error could be lethal if the research directly impacts the person such as in drug therapy. The higher the probability of error the less valid the study. According to Houser, the acceptable alpha rate is a level of significance less than 0.05. Type II error is the beta or statistical testing power in statistics (Houser, 2018). This is when the researcher reports there were no impacts or outcomes of the treatment or problem statement when actually there were. One of the ways to mitigate this error is to insure a large enough sample size. The researcher does not acknowledge there were findings to the intervention in the study. To maintain the integrity of internal validity is to have a solid design study. The researcher should be proactive in mitigating any potential barriers or outcomes by identifying a plan to mitigate their influences. One way doing this is to have solid selection of the participants. The researcher could inject bias into the study by means of the selection process. An example of this would be when picking participants to fit into a too narrowed of a testing group or too broad. Professionally, I encountered this issue recently regarding a Hep C medication with one of my patients. He is a TBI patient and the study on this particular drug does not include TBI population, yet the drug is still being used. Could this be a question in the internal validity regarding treatment of the published data? From the nursing perspective I would question the validity of the drug and its published side effects. Overall, there are other factors in maintain internal validity such as consents, attrition, instruments used, and historical effects are some factors to consider. Houser, J. (2018). The importance of research as evidence in nursing. In Nursing research reading, using, and creating evidence (4th ed.). Burlington, MA: Jones and Bartlett Learning. Grand Canyon University. (2017, October 26). NUR 504: Week 6 | Descriptive, Inferential, and Multivariate Statistics [on line]. Retrieved from Number 2 Type I and type II errors are errors that involve the null hypothesis. A null hypothesis is “a hypothesis stating no relationship between the variables under study; used primarily in statistical testing as the hypothesis to be rejected” (Polit & Beck, 2017). Type I error is when researchers discover a false positive or they have rejected the null hypothesis even though it is actually true. A type II error is when researchers do not reject the null hypothesis even though the alternate hypothesis is the correct one. This is known as a false negative. When errors occur, they cause the research and results to be misleading. The researcher is responsible for looking at all the data and establishing the risk of error. If there is a type I error, then looking at medications affecting a disease, the researcher would say the drug helps with the disease. In reality, the drug does not kill the disease at all. In a type II error, it would be opposite of a type I error. The researchers would conclude that the drug had no affect on the disease when it actually would kill it. The researcher must test their data throughly and validate their results. Researchers must accept the results of their studies, instead of working to try and validate their hypothesis. When they validate their statistical data, they are making sure there is the least amount of errors possible. Reference: Polit, D. F., & Beck, C. T. (2017). Nursing research: Generating and assessing evidence for nursing practice (10 th ed.). Philadelphia, PA: Lippincott Williams & Wilkins. Number 3 Type I errors are equivalent to false positives. It encompasses the denunciation of a null hypothesis that is actually true. It occurs when we are observing a difference when in truth there is none. Meaning there is no statistically significant difference. A type II errors are equivalent to false negative. It is a statistical term used within the framework of hypothesis testing that describes the error that occurs when one accepts a null hypothesis that is actually false. This is the error of failing to accept an alternative hypothesis when you don’t have adequate power. Meaning, it occurs when we are failing to observe a difference when in truth there is one. The error rejects the alternative hypothesis, although it does not occur due to chance. A type II error fails to remove, or accepts, the null hypothesis, although the alternative hypothesis is the true state of nature. The chance of getting type II error can be decrease, by increasing the sample size (Polit & Beck 2017). Type I and type II errors are part of the process of hypothesis testing. Although the errors cannot be eliminated, we can minimize one type of error. When conducting a hypothesis test, the probability, or risks, of making a type I error or type II error should be considered. Name one thing that can be done to improve internal validity of a study. Internal validity refers to how well an experiment is done, especially whether it avoids more than one possible cause acting at the same time. The less chance for this to happen in a study, the higher its internal validity is. Internal validity can be improved in a few simple ways. One way is for investigators to use single and double-blind techniques. A single-blind study is where the participant does not know the condition they are in, and double-blind is where neither the participant nor the experimenter knows what the groups represent. This method ensures there is little demand characteristics, such as trying to behave a certain way because they think that is what is expected of them, and decreases researchers’ effects, as they cannot even accidently have a bias to a certain group. Polit, D., & Beck, C. (2017). Nursing research. Generating and assessing evidence for nursing practice (10th ed.). [Grand Canyon University Digital Resources]. Retrieved from: Number 4 A Type I error is when you reject the null hypothesis even if it is true (false positive) and a Type II error is when the null hypothesis is not true and is accepted (false negative/ failing to reject). Example: Type I error: When a person does not have breast cancer, yet the mammogram says she does. Type II error: When a person does have breast cancer, yet the mammogram fails to pick it up. These errors are important because sometimes, by chance alone, a sample is not representative of the population. Then the results of the sample does not reflect the reality in the population, and the random error leads to an erroneous inference (Banerjee, Chitnis, Jadhay, Bhawalkar & Chaundhury, 2009). Internal validity refers to the extent to which it is possible to make an inference that the independent variable, rather than another factor, is truly causing variation in the dependent variable (Polit & Beck, 2015). Having a strong framework and being open about all the biases that can arise is an important factor in improving internal validity. Basically, “covering all your bases” and having a “plan B”. Often, researchers identify the research problem and then go in search of a theory. I think if they also studied the cause and then the effect, they would see all possible tangents occurring from the research. Assignment:Response to Discussion regarding Type 1 and Type II errors Reference Banerjee, A., Chitnis, U., Jadhay, S., Bhawalkar, J., and Chaundhury, S. (2009). Hypothesis testing, type 1 and type 11 errors. Industrial Psychiatry Journal . 18(2), 127-131. Retrieved from: Polit, D & Beck, C. (2012). Nursing research: Generating and assessing evidence for nursing practice. Philadelphia, PA: Lippincott Willians & Wilkins Get a 10 % discount on an order above $ 100 Use the following coupon code : NURSING10

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