Learn how to test a hypotheses in four steps, including developing hypotheses, identifying the test statistic, using the p-value, and comparing the significance level to the p-value.
- The adult residents of a large town…with an adult population of 35,000…are half male and half female.…Each week, 50 adults are chosen at random…to participate in jury duty.…Women have complained that they are getting called…to jury duty more often than the men.…Jury administrators contend the system is random and fair.…A committee is setup to investigate.…They use the next jury pool as a sample.…
They find that in that pool of 50 potential jurors,…14 are men and 36 are women.…The jury administration contends this happened by chance.…A lobbying group disagrees.…What we have here is an excellent opportunity…to utilize hypothesis testing.…Hypothesis testing is really a process.…In our case, a four step process.…So, in an effort to walk you through the process,…we're going to keep the math simple…and focus on what's happening in the individual steps.…
So let's get started.…In our first step, we need to setup our hypotheses.…There will typically be two hypotheses.…H sub-zero or H not,…this is our null hypothesis.…We might refer to this as what we consider…
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.