Hypothesis tests are sometimes wrong. This video provides an explanation of how that might happen and the different types of errors possible, as well as a discussion of false positives and false negatives.
- In our hypothesis tests,…we've always set up a null hypothesis…and an alternative hypothesis.…The null hypothesis typically assumes…that the status quo prevails.…The null hypothesis might state that the system works,…it might tell us that nothing has changed in our system.…Our alternative hypothesis assumes the opposite.…The alternative hypothesis might tell us…that the system is broken.…It might tell us that things have changed.…Let's use a special type of cancer screening test…as an example.…
This fictional screening would provide a reading…based on your blood.…The average reading is 100.…People that get a reading over 125 get a positive result.…This would indicate they have cancer.…If we were going to equate this to a hypothesis test,…we would say the cancer screening had two hypotheses.…The null hypothesis would be that everything is okay.…The person being tested does not have cancer.…
The alternative hypothesis would state…that the person being tested does, in fact, have cancer.…Let's say that the incidence of cancer…
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.