One sample is all you need to create a confidence interval that might help you understand an entire population.
- At this point, you should hopefully feel comfortable…with the concepts of sample size…and the central limit theorem.…The central limit theorem tells us…that if we take enough simple random samples,…we can get an excellent approximation…of our population means.…In other words,…rather than measure everything in the population,…we can take some random samples.…Those random samples will provide us…with the measurements that will be nearly normal…in our distribution,…and will direct us to the population mean.…
And you also, hopefully, remember…that the larger the sample size of those random samples,…the smaller the standard deviation of our distributions,…so the more certain we are…about our resulting population mean.…Lots of samples make us feel confident…about our population numbers.…I have so many data points,…the evidence to support our approximation is very strong.…In this section,…in which we will cover confidence intervals,…we're going to go in the opposite direction.…
What happens when we have only one sample?…
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.
- Data and distributions
- Sample size considerations
- Random sampling
- Confidence intervals
- Hypothesis testing