Estimation and InferenceLocked: included in All Access

How to learn about a population from a sample: the five main sampling methods and what they mean for sampling error, why sample size and a single population matter, the central limit theorem and the standard error of the sample mean, confidence intervals for the mean (z vs t), and bootstrap and jackknife resampling.

0/8 lessons
~106 min1 videoStart
Flashcards 45 cardsOpen
  1. 1. Populations, samples and sampling errorWe study a sample because examining the whole population is impossible or too costly, and the price we pay is sampling error.Locked: included in All Access13 min
  2. 2. Stratified and cluster samplingStratified sampling draws randomly from every subgroup to guarantee representation and precision; cluster sampling picks whole subgroups to save time and money.Locked: included in All Access14 min
  3. 3. Convenience and judgmental sampling, and choosing a methodNon-probability samples are chosen by convenience or expert judgment: quick and cheap, but at real risk of not representing the population.Locked: included in All Access12 min
  4. 4. Sample size and mixing populationsMore data reduces sampling error, but with diminishing returns, and only if every observation comes from the same population.Locked: included in All Access12 min
  5. 5. The central limit theoremWhatever the shape of the population, the mean of a large random sample is approximately normally distributed around the true mean, with variance σ2/n\sigma^2/n.Locked: included in All Access13 min
  6. 6. The standard error of the sample meanThe standard error measures how precisely a sample mean estimates the population mean: σ/n\sigma/\sqrt{n}, or s/ns/\sqrt{n} when σ\sigma is unknown.Video · 6 minLocked: included in All Access13 min
  7. 7. Confidence intervals for the meanA confidence interval turns a point estimate into a range: point estimate ± reliability factor × standard error.Locked: included in All Access15 min
  8. 8. Bootstrap and jackknife resamplingResampling builds the sampling distribution of a statistic by computer from the one sample you have, with no analytical formula needed.Locked: included in All Access14 min

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Estimation and Inference · Academy · CheapMocks