BUS308 Week 5 Discussion 1 Confidence Intervals/Effect Sizes

React to the material in the lecture.

In previous lectures, we learned how to conduct the null hypothesis significance testing (NHST). The P values lie at the center of NHST and are utilized to characterize or calculate probability into two groups: “has a significant effect” or “doesn’t have a significant effect.” NHST is every now and again scrutinized for its misinterpretation of relationships and confinements in surveying practical importance. A confidence interval is a range of values that, based upon the sample results, most likely contains the actual population parameter

What is new? One of the major parts of inferential statistics is the development of ways to calculate confidence intervals. Confidence intervals provide us with a way to estimate a population parameter. Rather than say that the parameter is equal to an exact value, we say that the parameter falls within a range of values. This range of values is typically an estimate, along with a margin of error that we add and subtract from the estimate. Concluding with Effect Size which calculates in the different statistical test the value translation into large, moderate and or small labels.

Is there anything you found to be unclear? If, the top ¼ of one interval and the bottom ¼ of the other overlap, then we have a significant difference at the alpha = 0.05 level. If the endpoints barely overlap, why is the significant difference around the alpha = 0.01 level?

How could you relate these ideas to issues and problems within your degree area? The U.S. Census Bureau routinely uses confidence levels of 90% in their surveys. One survey of the number of people in poverty in 1995 stated a confidence level of 90% for the statistics “The number of people in poverty in the United States is 35,534,124 to 37,315,094.” That means if the Census Bureau repeated the survey using the same techniques, 90 percent of the time the results would fall between 35,534,124 and 37,315,094 people in poverty.

Reference

Dong Kyu Lee. (2016). Alternatives to P-value: confidence interval and effect size. Korean Journal of Anesthesiology, 69(6), 555–562. https://doi-org.proxy-library.ashford.edu/10.4097/kjae.2016.69.6.555

Khan Academy. (n.d.). Statistics and probability. Retrieved from https://www.khanacademy.org/math/statistics-probability