This video helps viewers understand how we can find the means for large populations without measuring the entire population.
- Suppose we know that the average player…in a men's college basketball league weighs 180 pounds.…Let's also say that the median player…weighs about 190 pounds, so that means…quite a few of the smaller players in the league…are bringing down that average.…This league has over 4,000 players.…Would we have to weigh everyone of those 4,000 plus players…to know the average weight of a player in the league?…Well, if you remember, the Central Limit Theorem…tells us that by taking some simple random samples,…we can get a very good approximation…of the true population average.…
If we take five random samples,…with a sample size of only four,…we might find that those five tiny samples…will have sample means that average to perhaps 182 pounds.…Now, if we take five random samples,…but increase the sample size to 25,…we would likely see the mean of the sample means…closer to 180.5 pounds.…Don't believe me?…Try this yourself.…
There are plenty of online simulations…that will run these types of random…experiments for you.…That's actually how I came up with my numbers.…
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.