What exactly is a random sample? How random is random? Learn about the challenges of getting a random sample.
- So often, we cannot gather the data…for an entire population.…It's either too expensive or just not possible.…Ideally, statisticians look to gather sample data…from parts of the population.…Actually, to get statistically reliable results,…statisticians need to make sure…their data was selected randomly from the population.…And the most dependable type of data comes…from what we call a simple random sample.…
This means that the sample is chosen…such that each individual in the population…has the same probability of being chosen…at any stage during the sampling process.…And each subset of k individuals…has the same probability of be chosen for the sample…as any other subset of k individuals.…But while the name tells us…gathering a truly random sample is simple, it's not.…
It's actually quite difficult.…Why?…Well, a simple random sample must exhibit…two key characteristics.…The sample must be unbiased,…and the data points must be independent.…Let's discuss what these two things are, and let's also see…why each characteristic can be so elusive.…
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