What is the standard error, how is it calculated, and what role does sample size play in the size of the standard error?
- We've already begun to see the impact of sample size.…In general, the larger the sample size, the more…confidence we have in our results.…Now let's shift our attention to the standard error.…In short, the standard error is the standard deviation…of our proportion distribution.…Through an example, let's take a look at how…we calculate the standard error and also…what that calculated number would mean to us.…
In the cell phone industry, companies struggle…to keep their clients happy.…Suppose a reputable, national poll…finds that 60% of adults are satisfied…with their cell phone provider.…Let's take that as our population proportion 0.60.…We'd like to see if cell phone service in our city…reflects what is being seen nationally.…In an attempt to measure this, we'll take simple random…samples of 100 cell phone users in our city.…
Now, we know that 100 people can't possibly reflect…the satisfaction levels of everyone in our city,…therefor we can assume that each sample will carry…with it some level of standard error.…
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.