Please provide a 250-word response to the following discussion with at least 1 resource.

Healthcare AdministrationGlobalization

Running Head: WEEK 4 DISCUSSION 1

DISCUSSION POST 4

Jennifer L. Naegele

Dr. Steven Szydlowski

HAD – 505

July 29, 2020

Discussion Board 5

Why is it necessary to adjust alpha (α) when preforming multiple F-tests following ANOVA to identify where the differences lie?

A confidence interval is a highly defined value range that produces parameter values within its specified probability. When speaking of the 95% confidence interval, we do not speak of definitive or absolute predictions or numbers. Applying the 95% confidence interval to the mean length of adult patient hospital stays does not state that a 95% chance of said interval exists or will occur. Confidence intervals are significant because they measure, or paint a picture, in terms of risk. More specifically, confidence intervals apply to sample sizes, methods and sheds light on potential variations in such numerical results and accuracy rates.

When a sample is taken, it should target the specific parameters we want to be measured. Because the 95% confidence interval applies to such specified parameters, it outputs data relating only to those parameters and results in uncertainty against the sample pool targeted. For example, if a surveyor reveals a 0% confidence level, they are stating that the same result is unlikely in the event of a survey repeat. If a surveyor shows a 100% confidence level, they have performed a census study rather than a sample study as a census study is the only avenue in which a 100% confidence interval can be obtained. The U.S. performs a census every so many years which accounts for the entire population. A method such as a census usually results in accurate results. Due to its nature, statistical studies rarely produce a 100% confidence level results. This is because the data collected isn’t driving the results; the method used is.

In this circumstance, the 95% confidence interval states that if you perform the same study repetitively, there is a 95% chance that the results will mirror the actual average length of stay for all patients treated at the hospital. Another strong example of the 95% confidence interval would be election poll results.

Running Head: WEEK 4 DISCUSSION 1

DISCUSSION POST 4

Jennifer L. Naegele

Dr. Steven Szydlowski

HAD – 505

July 29, 2020

Discussion Board 5

Why is it necessary to adjust alpha (α) when preforming multiple F-tests following ANOVA to identify where the differences lie?

A confidence interval is a highly defined value range that produces parameter values within its specified probability. When speaking of the 95% confidence interval, we do not speak of definitive or absolute predictions or numbers. Applying the 95% confidence interval to the mean length of adult patient hospital stays does not state that a 95% chance of said interval exists or will occur. Confidence intervals are significant because they measure, or paint a picture, in terms of risk. More specifically, confidence intervals apply to sample sizes, methods and sheds light on potential variations in such numerical results and accuracy rates.

When a sample is taken, it should target the specific parameters we want to be measured. Because the 95% confidence interval applies to such specified parameters, it outputs data relating only to those parameters and results in uncertainty against the sample pool targeted. For example, if a surveyor reveals a 0% confidence level, they are stating that the same result is unlikely in the event of a survey repeat. If a surveyor shows a 100% confidence level, they have performed a census study rather than a sample study as a census study is the only avenue in which a 100% confidence interval can be obtained. The U.S. performs a census every so many years which accounts for the entire population. A method such as a census usually results in accurate results. Due to its nature, statistical studies rarely produce a 100% confidence level results. This is because the data collected isn’t driving the results; the method used is.

In this circumstance, the 95% confidence interval states that if you perform the same study repetitively, there is a 95% chance that the results will mirror the actual average length of stay for all patients treated at the hospital. Another strong example of the 95% confidence interval would be election poll results.