Course Principles
Fosters active explorations through activities that lead students to explore
statistical ideas
Introduces topics through genuine data from real studies
Emphasizes issues of data collection and scope of conclusions from beginning

Highlights connections between data collection method and inference procedure

Models pedagogy and content that prospective K12 teachers will be expected
to teach

Utilizes simulation frequently, as a problemsolving tool and pedagogical
device

Presents handson, tactile simulations before technologybased ones
Uses a variety of computational tools, including statistical software and
spreadsheet packages
Incorporates Java applets specifically designed to accompany the learning
activity
Emphasizes development of students’ communication skills

Presents probability ideas in context of statistical applications

Repeatedly presents complete process of statistical investigation, from
data collection through interpretation of results

Preliminary Content Outline

Chapter 1: Comparisons and Conclusions – descriptive analyses of
2x2 tables (segmented bar graphs, conditional proportions, relative risk,
odds ratio), types of variables, observational studies vs. controlled experiments,
confounding variables, causation, randomization, Simpson’s paradox

Chapter 2: Experiments and Significance – simulating pvalue for
randomization test, probability, equal likeliness, combinations and permutations,
hypergeometric probabilities, Fisher’s exact test

Chapter 3: Sampling – population vs. sample, parameter vs. statistic,
sampling bias, precision, simple random sampling, binomial distribution,
binomial tests, effect of sample size, type I and II error, power

Chapter 4: LargeSample Approximations – normal approximations to
binomial, hypergeometric distributions, terminology and structure of significance
tests, ztest for a population proportion, ztest for a difference
in population proportions

Chapter 5: Estimation and Confidence – exact binomial confidence
intervals, approximate zintervals for a population proportion,
effects of confidence level and sample size, interpretation of confidence,
alternative procedures, sample size determination, CI for odds ratio

Chapter 6: Quantitative Variables – descriptive analyses (graphical
displays and numerical summaries), sampling distributions of sample mean
and other statistics, Central Limit Theorem, tdistribution, ttests
and tintervals for one and twosamples, robustness, prediction
intervals, transformations

Chapter 7: Association and Prediction – simple linear regression
(descriptive and inferential), logistic regression, oneway ANOVA, chisquare
tests of independence, homogeneity of proportions
