Different questions require different hypothesis tests. Some hypothesis tests require two-tailed tests, and others require one-tailed tests.
- [Narrator] Let's consider three different statements.…First, a recent national study…found that the average American…between the ages of 18 and 24…checks their phone 75 times per day.…A mobile service provider questions these results.…Second, the average amount of time it takes an adult…to recover from the common cold is 8.5 days.…A new medicine was tested on a sample…of adults suffering from the common cold.…
The average recovery time for the people in this group…was 7.3 days.…The company that developed this medicine…thinks the drug should be considered for federal approval.…Finally, consider the national average…for the college entrance exam, 1000 points.…The Regent Test Prep Academy claims…that their students consistently beat that national average.…These are all situations…where hypothesis testing would be useful.…
But each of these situations would require…a different type of hypothesis test.…Let's look at each situation individually.…In our first situation,…we had a claim that said people between the ages…
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