This video provides explanations and pros and cons of alternate samples: stratified, cluster, systematic, and opportunity.
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- The simple random sample.…It's so important to dependable statistical analysis.…But as we've seen, the simple random sample…can be rather elusive.…Eliminating bias and maintaining data independence…is quite challenging.…As a result, alternatives to the simple random sample…are sometimes utilized.…Now, before we explore these sampling alternatives,…I want to be sure you understand,…that the simple random sample is still the only way…to get dependable statistical outcomes.…
I can already hear you saying,…hey, if the simple random sample…is the only way to ensure dependable results,…why would anyone use these alternatives?…Well, these alternative methods are simpler to organize,…easier to carry out, and often,…they seem both logical and sound.…Let's quickly look at some of these…alternative sampling methods.…For a systematic sample, simply choose one unit…and then every k unit thereafter.…
So if we're measuring customer satisfaction at a store,…perhaps you might ask the first person…to come out of the store for their opinions,…
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