University of Wisconsin–Madison
Division of Pharmacy Professional Development
UW–Madison School of Pharmacy

Basic Statistics and Practical Pharmaceutical Applications

A person taking the basic statistics and pharmaceutical applications course

Enhance your pharmaceutical expertise with our Basic Statistics and Practical Pharmaceutical Applications course, offered by the University of Wisconsin–Madison’s Division of Pharmacy Professional Development.

Course Overview:

In the pharmaceutical industry, the appropriate use and interpretation of statistics are crucial for research and development. This 100% online, self-paced course is designed to equip non-statisticians with the practical knowledge needed to apply basic statistical tests to common industry challenges. Furthermore, you’ll learn to efficiently utilize statistics to enhance your decision-making processes through real-world examples and hands-on exercises using Excel.

Key Learning Objectives:

  • Fundamental Statistical Concepts: Understand and define common statistical terms and differentiate between descriptive and inferential statistics.
  • Data Presentation: Learn various graphic and numeric methods for presenting descriptive statistics effectively.
  • Probability and Distributions: Explore different distributions, practical applications of probability, and measures of central tendency.
  • Hypothesis Testing: Gain insights into hypothesis testing, significance testing, and sampling procedures to make informed decisions.
  • Statistical Test Application: Identify appropriate statistical tests for different variables and interpret computer-generated statistical outputs.
  • Excel Proficiency: Utilize Excel to evaluate pharmaceutical data, enhancing your analytical capabilities.

Course Format:

Designed for flexibility, this asynchronous distance education course allows you to enroll at any time and progress through the material at your own pace until December 31, 2026. Additionally, the curriculum includes a series of lectures complemented by practical examples and exercises, ensuring you can apply statistical concepts directly to your work.

Who Should Attend?

This course is ideal for pharmaceutical professionals seeking to strengthen their statistical skills, including:

  • Junior scientists without a biostatistics background.
  • Laboratory staff performing routine biostatistical analyses.
  • Individuals desiring a refresher on basic inferential statistics.

Why Enroll?

By completing this course, you’ll acquire essential statistical skills tailored to the pharmaceutical industry, enabling you to interpret data accurately and make evidence-based decisions. Moreover, the practical knowledge gained will enhance your research quality and contribute to the success of your projects.

Join us to advance your proficiency in pharmaceutical statistics and make a meaningful impact in your field.

Developed & Conducted by the Division of Pharmacy Professional Development, School of Pharmacy, University of Wisconsin- Madison

Registration Information

Register

Refund Policy

Course date:Mar 17, 2023 - Dec 31, 2026
Location:
Course fee:

$800
For this course you will need to purchase the book.

Academic and Non-profit: Please contact the program coordinator for more information.

Course Objectives

This asynchronous distance education course is designed to provide non-statisticians 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:

  1. t-tests
  2. analysis of variance and post hoc procedures
  3. measures of repeatability and reproducibility
  4. chi square and related tests
  5. correlation
  6. linear regression
  7. major nonparametric procedures
  8. 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:

  1. Define statistical terms commonly encountered in the literature;Online statistics course
  2. List the various graphic and numeric methods for presenting descriptive statistics;
  3. Describe various distributions, practical applications of probability, and the measures of central tendency;
  4. 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;
  5. Interpret statistical output from computer generated results and evaluate the relative importance of those outcomes; and
  6. Use Excel to evaluate pharmaceutical data.

Who Should Attend

This asynchronous distance education course is designed to provide non-statisticians with practical information needed to apply basic statistical tests to problems typically faced in pharmaceutical industry.

  • Junior scientists who do not have a biostats background 
  • Lab based staff who perform routine biostats analyses
  • Anyone who would like a refresher on basic inferential statistics

Course Outline

Lecture 1

An Introduction and Defining Variables

Objectives: Upon completion of this unit the learner should be able to:

  1. Differentiate between descriptive and inferential statistics;
  2. Determine if a variable is continuous or discrete;
  3. Identify independent and dependent variables in a statistical problem set; and
  4. Initiate the Excel data analysis option.

Lecture 2

Descriptive Statistics

Objectives: Upon completion of this unit the learner should be able to:

  1. List the ways to describe information for sample data for a discrete variable;
  2. List the ways to describe the central tendencies for a continuous variable sample; and
  3. Use Excel to produce descriptive statistics for continuous data.

Lecture 3

Sampling

Objectives:  Upon completion of this unit the learner should be able to:

  1. Discuss the advantages of using a random sample;
  2. Use a random numbers table to select a sample; and
  3. List probabilistic sampling methods

Lecture 4

An Introduction to Probability

Objectives:  Upon completion of this unit the learner should be able to:

  1. Discuss the ways to determine probability for an event;
  2. Explain conditional probability;
  3. Differentiate between combinations and permutations; and
  4. 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:

  1. Describe the use of sample data to estimate population parameters;
  2. Differentiate between the standard deviation and standard error for sample data;
  3. Create confidence intervals using sample data when the population standard deviation is known or can be estimated; and
  4. 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:

  1. Describe the difference between a null hypothesis and an alternative hypothesis;
  2. Define Type I and Type II errors in hypothesis testing; and
  3. List four factors that influence the power of a statistical procedure.

Lecture 7

Student t-tests

Objectives:  Upon completion of this unit the learner should be able to:

  1. Define the degrees of freedom for a one-sample, two-sample t-test and paired t-test;
  2. Calculate a confidence interval based on sample descriptive statistics;
  3. Determine the significance of continuous dependent data for
    two levels of a discrete independent variable;
  4. Identify paired versus unpaired data; and
  5. Use Excel to calculate a two-sample t-test and a paired t-test.

Lecture 8

One-way Analysis of Variance and Post Hoc Procedures

Objectives:  Upon completion of this unit the learner should be able to:

  1. Define the degrees of freedom associated with an analysis of variance;
  2. Determine the significance of continuous dependent data for more than two levels of a discrete independent variable;
  3. Use Excel to calculate a one-way analysis of variance;
  4. Discuss why post hoc procedures are important in interpreting statistical results;
  5. List appropriate post hoc procedure for a significant analysis of variance.

Lecture 9

N-way Analysis of Variance and Applications

Objectives:  Upon completion of this unit the learner should be able to:

  1. Determine the degrees of freedom for an N-way analysis of variance;
  2. Expand a two-way ANOVA to a measure of repeatability and reproducibility; and
  3. Use Excel to calculate a two-way analysis of variance.

Lecture 10

Correlation and Linear

Objectives:  Upon completion of this unit the learner should be able to:

  1. Define the difference between linear regression and correlation;
  2. Identify the type of variables associated with a correlation and contrast with linear regression;
  3. Determine the significance of a correlation coefficient;
  4. Describe the type of variability that is identified by a regression line;
  5. Evaluate the significance a linear regression calculation; and
  6. 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:

  1. Describe the use of the chi square test of independence when trying to evaluate the relationship between two discrete variables;
  2. Identify the number of degrees of freedom for various sizes of contingency table;
  3. Discuss the reasons for using a McNemar test; and
  4. 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:

  1. Describe the reasons why nonparametric test may be preferred over traditional parametric procedures;
  2. Identify nonparametric alternatives for the Student t-tests, one-way ANOVA and correlation; and
  3. Discuss the ranking procedure required for most nonparametric procedures.

Lecture 13

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:

  1. Describe how outlier tests can be used to determine if there is an aberrant value;
  2. Define the difference between an outlier and an influential data point; and
  3. Apply some of the most commonly used outlier tests

Instructors

James E. De Muth, PhD, RPh
Emeritus Professor
School of Pharmacy, University of Wisconsin-Madison

Program Coordinator

Eric Buxton, PhD

Division of Pharmacy Professional Development
777 Highland Avenue
Madison, WI 53705
(608) 262-2431 FAX
(608) 265-2259
eric.buxton@wisc.edu

Customized Course Request