The size of a sample is vital in knowing if your results are valuable. This video addresses issues of why larger sample size can be more valuable. Proportions are used in this example.
- A sample is a group of units…drawn from a population,…and the sample size is the number of units drawn…and measured for that particular sample.…The total population itself may be very large…or, perhaps, immeasurable, so a sample is…just looking at a slice of the population in the hopes…of providing us a representative picture…of the entire population.…As you might guess, the larger the sample size,…the more accurate our measurement or, at least,…the more confidence we have that our sample…is actually providing us a glimpse of the whole population.…
But just how important is sample size?…Let's first establish how an experiment might look.…Let's say we own a machine that manufactures forks.…The forks manufactured from this system…are either judged as acceptable or as defective.…You might remember this type of scenario from when…we looked at binomial random variables…in Stats Fundamentals One.…
Anyway, this magic fork-manufacturing machine,…over its entire existence, it will manufacture…90% good forks and 10% defective forks.…
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.