The central limit theorem helps us understand how data is likely to be distributed with large and small sample sizes.
- The central limit theorem.…Just saying the words can be a little intimidating.…It sounds complicated.…Perhaps something only a hardcore statistician…would dare study.…It turns out that it's a rather simple concept,…a concept that's not only important…to our world of statistics,…but dare I say,…it's also a concept that is rather interesting.…Let's start simple.…A distribution of discrete numbers.…
We start on the left,…where we have five values of five.…We move right along our distribution.…Two units of 10.…Four units of 15.…Six units of 20.…And on the right of our distribution,…we have three values of 25.…20 different readings in our entire population.…If we average out the values of our 20 different readings,…we get an average of 15.0.…
Now suppose we didn't want to tally up all 20 values,…but we still wanted to find the average of the data set.…Could we use samples to direct us to the population mean?…Let's try it.…Let's take samples of four units every day.…Here's our first sample.…Sample one, 10, 15, 20, 25.…Our sample mean for this sample is 17.5.…
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.