Please respond to the following: Explain why an increase in sample size will red

By

Please respond to the following:
Explain why an increase in sample size will reduce the probability of a type II error, but such an increase will not impact the probability of a type I error.
Support your reasoning using a scholarly source. Visit the Strayer University Library for help.
Be sure to respond to at least one of your classmates’ posts. Cite any resources used.
Respond to the classmate below:
Yoshekia Roy RE: Week 3 Discussion
Explain why an increase in sample size will reduce the probability of a type II error, but such an increase will not impact the probability of a type I error.
When I think of a hypothesis, what comes to my mind first is the science projects I used to do when I was in middle and high school. Hypothesis testing is the process used to evaluate the strength of evidence from the sample and provides a framework for making determinations related to the population. The investigator formulates a specific hypothesis, evaluates data from the sample, and uses the data to decide whether they support the particular hypothesis. A good hypothesis must be based on a good research question. It shouldn’t be a long, drawn-out question. Instead, it should be specific, simple, and stated in advance. Testing begins by considering two hypotheses. These hypotheses viewpoints are opposing:
Null hypothesis- there is no association between the outcome and the predictor variables in the population. Alternative hypothesis-describes the existence of an association and is typically what the investigator would like to show. Since the alternative and null hypotheses are contrary, you must examine evidence to determine if you have enough evidence to reject the null hypothesis or not. The evidence is in the sample data form. An investigator’s conclusions can be wrong. At times, a sample may not be representative of the population. A type I error, also known as a false positive, occurs of an investigator rejects a null hypothesis that is true in the population. A type II error, also known as a false negative, occurs if the investigator fails to reject a null hypothesis that is false in the population (Banerjee,Chitnis,Jadhav,Bhawalkar, Chaudhury, 2009). Type I and Type II errors can not be avoided, but an investigator can reduce the likelihood of the happening by increasing the sample size. The larger the sample size, the lesser is the probability that it will differ substantially from the population.
The chances that a study will be able to detect an association between an outcome variable and a predictor variable depends on the size of the target population. If the sample size is large, it will be easy to detect. If the sample size is small, it will be more challenging to detect in the sample. When the investigator chooses the size of the association he would like to detect in the sample, the quantity is known as the effect size. The probability of committing a type I error is called alpha or the level of statistical significance. The probability of making a type II error is called beta. The quantity of beta is called power (Davis & Mukamal, 2006).
Real life example of Type II and Type 1 Error
This personally happened to me. I was having all the symptoms of COVID and decided it was best I get tested. Within one hour after my testing, I got the results saying I was positive. I also made another appointment the same day with my PCP, got tested, and was negative within a five-hour span. I was a bit confused, so I waited until the next day and went to my local ER. By now, I was running a fever, and I knew something was wrong. There I tested positive again for the second time. The type I error here was a false positive. The results stated I was positive for COVID but didn’t have the virus. Type II error was a false negative. The test results at my PCP stated that I didn’t have COVID, but when I went into the ER I was positive for COVID.
References
Banerjee, A., Chitnis, U. B., Jadhav, S. L., Bhawalkar, J. S., & Chaudhury, S. (2009). Hypothesis testing, type I and type II errors. Industrial psychiatry journal, 18(2), 127–131. https://doi.org/10.4103/0972-6748.62274
Hypothesis Testing | Circulation. https://www.ahajournals.org/doi/full/10.1161/circulationaha.105.58646
NOTE: Please add references if any.

How to create Testimonial Carousel using Bootstrap5

Clients' Reviews about Our Services