It is often not feasible to capture parameters for an entire population; however, it’s necessary to gather statistics to estimate population parameters. In this course, you will walk through the multiple methods of collecting samples and examining margin of error and confidence intervals, including how they are calculated. You will then explore another area of inferential statistics called hypothesis testing to start with a hypothesized value. One of the most important measures to calculate is the p-value, which helps gauge the significance of your findings. You will observe the role that p-values play in hypothesis testing and the way in which they are calculated.
WHAT YOU'LL LEARN
Examine margin of error and confidence intervals, including how they are calculated
Review the criteria for determining sample sizes and complete an exercise on interpreting confidence intervals
Explore the six steps of hypothesis testing, from developing hypotheses to stating a conclusion
Identify the means of testing a hypothesis and complete a sample test
Observe the role that p-values play in hypothesis testing and the way in which they are calculated
Practice computing p-values and interpreting them in Excel
Cindy van Es is professor of practice in the Dyson School of Applied Economics and Management. She has a PhD in statistics from Iowa State University, and joined Cornell in 1988. She teaches three courses in the undergraduate business program: Introductory Statistics, Business Statistics, and Impact Learning: South Africa. Her general area of interest is statistical education, with a focus on business applications and teaching through social justice examples.
She currently serves as director of Dyson’s Undergraduate Business Program. In this position, she provides strategic leadership and supervision on activities within the undergraduate program at the school, focusing specifically on implementation of the undergraduate curriculum and review of academic policies