Unit 10: Confidence Intervals

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Exploratory Data Analysis
  • Confidence intervals and inference help students use data to just answer that simple investigative question that they may have developed about a particular population. This should reference back to the first unit of the course.

  • In order to evaluate whether assumptions hold, need to look at shape, center, spread of sample distribution.
  • Confidence intervals help us make inference about a population using just a single sample. Just as the last unit discussed, it is important to reiterate that we just need one sample to make inference.

  • Confidence intervals rely on sampling distributions and the Central Limit Theorem (if assumptions are met), so it is important to show students how these concepts all relate.

  • For assumptions to be met, we need to follow good data collection practices to obtain a random sample.

  • If the Central Limit Theorem applies, students will need to recall normal models and z-scores. Remember: their sample may not be normal, but the sampling distribution of their sample mean or proportion is normal under Central Limit Theorem.