Resource Library

The Numbers Guy examines numbers in the news, business and politics. Some numbers are flat-out wrong or biased, while others are valid and help us make informed decisions. Carl Bialik tells the stories behind the stats, in daily updates on this blog and in his column published every other Friday in The Wall Street Journal.

This lecture example discusses calculating chance with probabilities (a ratio of occurrence to the whole) or odds (a ratio of occurrence to nonoccurrence). It presents a clinical example of measuring the chance of initiating breastfeeding among 1000 new mothers. Tables are provided in pdf format.

This lecture example discusses type I and type II errors as they apply in a clinical setting.

This lecture example reviews the concept of CIs and their relationship to P values. Tables are provided in pdf format.

This lecture example discusses how two continuous variables relate to one another with a clinical example of the relationship between body mass and fasting blood sugar. It offers three questions to help readers visualize and interpret correlation coefficients.

Because surveys are increasingly common in the medical literature, readers need to be able to critically evaluate the survey method. Two questions are fundamental: 1) Who do the respondents represent? 2) What do their answers mean? This lecture example discusses survey sampling terms and aspects of interpreting survey results.

This NSF funded project provides worksheets and laboratories for introductory statistics. The overview page contains links to 9 worksheets that can be done without technology, which address the topics of obtaining data, summarizing data, probability, regression and correlation, sampling distributions, hypothesis testing and confidence intervals. The page also contains twelve laboratories that require the use of technology. Data sets are provided in Minitab format.

This lesson introduces two sample hypothesis testing for means and discusses the one-tailed and two-tailed t-tests.

This applet relates the pdf of the Normal distribution to the cdf of the Normal distribution. The graph of the cdf is shown above with the pdf shown below. Click "Move" and the scroll bar will advance across the graph highlighting the area under the pdf in red. The z-score is shown as well as the probability less than z (F(z)) and the probability greater than z (1-F(z)).

This applet draws one-dimensional Brownian motion. Click the mouse in the window to start zooming. Click again to stop. Since Brownian motion is self-similar in law, all of the zoomed pictures look the same.