Understand statistics as a process for inference

What you'll learn

1. Distinguish a population from a sample and a parameter from a statistic, using correct notation (mu and x-bar for means, p and p-hat for proportions)
2. Describe statistics as a four-step process for inferring an unknown population parameter from a random sample
3. Explain why a random sample is necessary for valid inference, and identify the bias that arises when sampling is not random
4. Recognize sampling variability -- that a statistic changes from sample to sample while the parameter stays fixed -- and explain why this makes inference uncertain rather than exact
5. State what can and cannot be concluded from a single sample

Prerequisites

• Represent data with plots
• Compare center and spread of data sets