Course Objectives
This asynchronous distance education course is designed to provide nonstatisticians with practical information needed to apply basic statistical tests to problems typically faced in pharmaceutical industry. Emphasis will be placed on the most commonly utilized statistical tests, including:
 ttests
 analysis of variance and post hoc procedures
 measures of repeatability and reproducibility
 chi square and related tests
 correlation
 linear regression
 major nonparametric procedures
 outlier tests
The course focuses on the efficient utilization of statistics; not abstract theory. Practical examples drawn from scientific research will be used to illustrate statistical concepts. Emphasis will be placed on the appropriate use of statistics and the interpretation of their results and presents an overview of basic elements associated with statistical tests and their practical application. Numerous examples using Excel will be presented throughout the program.
Goals and Objectives (each lecture will have its own set of learning objectives):
Upon completion of the program the learner will be able to:
 Define statistical terms commonly encountered in the literature;
 List the various graphic and numeric methods for presenting descriptive statistics;
 Describe various distributions, practical applications of probability, and the measures of central tendency;
 Explain the use of hypothesis testing, significance testing, and sampling procedures;evaluate example problems to identify the type of variables involved and the most appropriate statistical test(s) to use;
 Interpret statistical output from computer generated results and evaluate the relative importance of those outcomes; and
 Use Excel to evaluate pharmaceutical data.
Who Should Attend
This asynchronous distance education course is designed to provide nonstatisticians with practical information needed to apply basic statistical tests to problems typically faced in pharmaceutical industry.
Course Requirements
Computer with speakers and internet.
Course Outline
Lecture 1

An Introduction and Defining Variables
Objectives: Upon completion of this unit the learner should be able to:
 Differentiate between descriptive and inferential statistics;
 Determine if a variable is continuous or discrete;
 Identify independent and dependent variables in a statistical problem set; and
 Initiate the Excel data analysis option.

Lecture 2

Descriptive Statistics
Objectives: Upon completion of this unit the learner should be able to:
 List the ways to describe information for sample data for a discrete variable;
 List the ways to describe the central tendencies for a continuous variable sample; and
 Use Excel to produce descriptive statistics for continuous data.

Lecture 3

Sampling
Objectives: Upon completion of this unit the learner should be able to:
 Discuss the advantages of using a random sample;
 Use a random numbers table to select a sample; and
 List probabilistic sampling methods

Lecture 4

An Introduction to Probability
Objectives: Upon completion of this unit the learner should be able to:
 Discuss the ways to determine probability for an event;
 Explain conditional probability;
 Differentiate between combinations and permutations; and
 Describe and apply the binomial distribution.

Lecture 5

Confidence Intervals and Tolerance Limits
Objectives: Upon completion of this unit the learner should be able to:
 Describe the use of sample data to estimate population parameters;
 Differentiate between the standard deviation and standard error for sample data;
 Create confidence intervals using sample data when the population standard deviation is known or can be estimated; and
 Describe tolerance intervals and how they differ from confidence intervals.

Lecture 6

Hypothesis Testing
Objectives: Upon completion of this unit the learner should be able to:
 Describe the difference between a null hypothesis and an alternative hypothesis;
 Define Type I and Type II errors in hypothesis testing; and
 List four factors that influence the power of a statistical procedure.

Lecture 7

Student ttests
Objectives: Upon completion of this unit the learner should be able to:
 Define the degrees of freedom for a onesample, twosample ttest and paired ttest;
 Calculate a confidence interval based on sample descriptive statistics;
 Determine the significance of continuous dependent data for
two levels of a discrete independent variable;
 Identify paired versus unpaired data; and
 Use Excel to calculate a twosample ttest and a paired ttest.

Lecture 8

Oneway Analysis of Variance and Post Hoc Procedures
Objectives: Upon completion of this unit the learner should be able to:
 Define the degrees of freedom associated with an analysis of variance;
 Determine the significance of continuous dependent data for more than two levels of a discrete independent variable;
 Use Excel to calculate a oneway analysis of variance;
 Discuss why post hoc procedures are important in interpreting statistical results;
 List appropriate post hoc procedure for a significant analysis of variance.

Lecture 9

Nway Analysis of Variance and Applications
Objectives: Upon completion of this unit the learner should be able to:
 Determine the degrees of freedom for an Nway analysis of variance;
 Expand a twoway ANOVA to a measure of repeatability and reproducibility; and
 Use Excel to calculate a twoway analysis of variance.

Lecture 10

Correlation and Linear
Objectives: Upon completion of this unit the learner should be able to:
 Define the difference between linear regression and correlation;
 Identify the type of variables associated with a correlation and contrast with linear regression;
 Determine the significance of a correlation coefficient;
 Describe the type of variability that is identified by a regression line;
 Evaluate the significance a linear regression calculation; and
 Use Excel to calculate correlation and linear regression

Lecture 11

Chi Square and Related Tests
Objectives: Upon completion of this unit the learner should be able to:
 Describe the use of the chi square test of independence when trying to evaluate the relationship between two discrete variables;
 Identify the number of degrees of freedom for various sizes of contingency table;
 Discuss the reasons for using a McNemar test; and
 Discuss the reasons for using a Fisher’s exact test.

Lecture 12

Nonparametric Tests
Objectives: Upon completion of this unit the learner should be able to:
 Describe the reasons why nonparametric test may be preferred over traditional parametric procedures;
 Identify nonparametric alternatives for the Student ttests, oneway ANOVA and correlation; and
 Discuss the ranking procedure required for most nonparametric procedures.

Lecture 13

Test for Outliers and Course Summary
Lynn Van Campen, PhD

Lecture 14

The second 'C' in "CMC"  Analytical Methods Development and Quality Control
Eugene J. McNally, PhD

Lecture 15

Drug Product  Analytical Controls for Different Dosage Forms  Setting Specifications for the Certificate of Analysis
Objectives: Upon completion of this unit the learner should be able to:
 Describe how outlier tests can be used to determine if there is an aberrant value;
 Define the difference between an outlier and an influential data point; and
 Apply some of the most commonly used outlier tests

Instructors
James E. De Muth, PhD, RPh
Emeritus Professor
School of Pharmacy, University of WisconsinMadison