How do we provide a window of results that would very likely encompass the actual population proportion? Learn more in this video.
- So there's an upcoming election for mayor…of a large city between two candidates.…Let's simply call the candidates Candidate A…and Candidate B.…You work for Candidate A.…Candidate A's campaign team wants to know…where Candidate A stands…so they ask you to conduct a poll.…You gather a random sample of 100 voters…and ask if they will be voting for Candidate A…or Candidate B.…55% of the 100 voters polled…said they planned on voting for Candidate A.…
Anything over 50% in the real election…would result in a win for your candidate.…So far, based on the results of the poll,…things look promising for your candidate…but remember, this was just one sample…with a sample size of 100.…Now, look, I understand that my small pre-election poll…likely didn't provide the actual percentage of votes…Candidate A will get on the actual day of the election…but maybe we're close.…
So let's create a 95% confidence interval.…In other words, let's use our sample result…to create an interval that very likely includes…the actual percentage of votes…
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.
- Data and distributions
- Sample size considerations
- Random sampling
- Confidence intervals
- Hypothesis testing