Suppose a 95% confidence interval is provided, but then the actual outcome lies outside of the given interval. What are the possible explanations for the unexpected outcome?
- Suppose before an election,…a polling organization reports…a 95% confidence interval for candidate A.…This confidence interval stretches from 0.51 to 0.54.…In other words,…the poll believes that Candidate A…will get between 51% and 54% of the vote.…Then election day comes around and Candidate A looses.…Candidate A would probably be furious.…Before the election,…they were very confident of a win.…
And now they realize that they actually lost.…How could this have happened?…Well this is where it helps…to be a well rounded statistician.…Beyond having a knowledge of the numbers and formulas,…you need to understand the real environment…that surrounds the poll.…In this case, it would be helpful if we understood…how political polls are done…and also the nature of the actual election.…What might go wrong during the actual poll?…Lying,…Correspondents might want to throw off the polls,…they may just lie.…
Or perhaps, they're embarrassed to tell…a pollster about their true opinions,…and thus they would rather give them…an answer that would please the pollster.…
Eddie Davila first provides a bridge from Part 1, reviewing introductory concepts such as data and probability, and then moves into the topics of sampling, random samples, sample sizes, sampling error and trustworthiness, the central unit theorem, t-distribution, confidence intervals (including explaining unexpected outcomes), and hypothesis testing. This course is a must for those working in data science, business, and business analytics—or anyone else who wants to go beyond means and medians and gain a deeper understanding of how statistics work in the real world.
- List the three primary issues addressed in Statistics Foundations: 2.
- Recognize two key characteristics associated with simple random samples.
- Apply the Central Limit Theorem to find the average of sample means.
- Analyze random samples during hypothesis testing.
- Assess individual situations to determine whether a one-tailed or two-tailed test is necessary.
- Define acceptance sampling.