Data Analysis for the Social Sciences / Module 03
Module 03 · Sampling & Inference
Sampling & Inference
How we move from a sample to a claim about a population — sampling distributions, standard errors, and confidence intervals, built up through simulation in R.
Module
Weeks5–6
CourseSOC 334
FormatLecture + Lab
Est. time4–6 hrs
Overview
A sample is not the population, yet we routinely use one to speak about the other. This module builds the bridge. We simulate repeated sampling to watch a sampling distribution emerge, define the standard error as its spread, and use it to attach honest intervals to our estimates — then practice reading, and misreading, confidence intervals.
Learning Goals
- Explain what a sampling distribution is and why it matters.
- Compute and interpret a standard error.
- Construct and correctly interpret a 95% confidence interval.
- Recognize common misinterpretations of inference.
Readings
- RequiredCourse text, ch. 8 — “From Sample to Population.”
- RequiredCourse notes — “Standard errors by simulation.”
- OptionalReinhart, Statistics Done Wrong, ch. 1–2.
Assignment
Problem Set 3 — estimate a population mean from a public dataset, report a 95% confidence interval, and write one paragraph interpreting it for a non-statistical reader. Due end of Week 6.
