Learning Outcome Statements

1. Introduction

a. define simple random sampling and a sampling distribution;

b. explain sampling error;

c. distinguish between simple random and stratified random sampling;

2. Time-Series and Cross-Sectional Data

d. distinguish between time-series and cross-sectional data;

3. The Central Limit Theorem

e. explain the central limit theorem and its importance;

4. Standard Error of the Sample Mean

f. calculate and interpret the standard error of the sample mean;

5. Estimators

g. identify and describe desirable properties of an estimator;

h. distinguish between a point estimate and a confidence interval estimate of a population parameter;

6. Confidence Intervals for the Population Mean

i. describe properties of Student's t-distribution and calculate and interpret its degrees of freedom;

j. calculate and interpret a confidence interval for a population mean, given a normal distribution with 1) a known population variance, 2) an unknown population variance, or 3) an unknown variance and a large sample size;

7. Common Biases in Sampling Methods

k. describe the issues regarding selection of the appropriate sample size, data-mining bias, sample selection bias, survivorship bias, look-ahead bias, and time-period bias.