The course treats several main topics. Descriptive statistics provides the language for discussing data. Statistical experimental design is the art and science of producing reliable statistical data. The course discusses the principle causes for bias in the collection of data and in the design of experiments, as well as the techniques available to avoid bias. This analysis involves the concepts of bias, controls, randomization, variability, and statistical significance.
Probability analyzes randomness.
Its use in statistical analysis allows for the quantification of statistical significance, accuracy, and confidence. Statistical inference involves the application of descriptive statistics and probability to the drawing of statistically significant conclusions about populations and processes. For example, using statistical tests and confidence intervals, statisticians can estimate population parameters precisely, and give measures of both the precision of the estimates and the confidence they have in these estimates. Homework assignments involve regular use of computers, especially the statistical package Minitab. It is recommended for all students who wish to learn about statistics and about how and why it works.