It has been more than a decade since the American Statistical Association (ASA) approved a set of curriculum guidelines ( for undergraduate statistics programs. Since that time, much has changed. The number of people receiving undergraduate degrees in statistics has increased dramatically. Interest in the AP Statistics program has flourished. The “Age of Big Data” has arrived. New tools have become available for statisticians. ASA now offers accreditation for professional statisticians. It is a good time to rethink the undergraduate curriculum.

ASA President-elect Nathaniel Schenker has appointed a working group consisting of representatives from academia, industry, and government to make recommendations for changes in the guidelines.
The working group welcomes your input.

Feedback also can be sent to the working group chair, Nicholas Horton, at


May we contact you via e-mail if we have follow-up questions regarding your recommended feedback?


If Academic: Does your department have a statistics major, minor, or emphasis?

If Academic: What statistics related degrees are offered at your institution?

If Academic: What is the web address describing your undergraduate statistics program to interested students?

If Academic: Who would you suggest we contact for additional information about your undergraduate statistics program (for example, Statistics Advisor, Undergraduate Coordinator)?

If Academic: In your opinion is your institution looking to add, subtract, or make no changes to the resources (program, FTEs, courses) in your undergraduate statistics program in the next 5 years?

If Non-Academic: How many statisticians are in your direct workgroup?

If Non-Academic: What is the distribution of statistics employees in your group by degree? (Please check that the sum equals the number of statisticians in your direct workgroup answered in the previous question.)

If Non-Academic: Do you advertise entry-level positions for undergraduates (BS with no experience) in your group?

If Non-Academic: Who would you suggest we contact for recent entry-level job or internship announcements targeting undergraduates in your group?

Please provide feedback and recommend updates for the current guidelines. Below are the guidelines and we ask for your feedback on the following four sections (curriculum guiding principles, skills needed, curriculum topics, and curriculum topics for minors or concentrations).

Curriculum Guidelines for Undergraduate Programs in Statistical Science
The American Statistical Association endorses the value of undergraduate programs in statistical science, both for statistical science majors and for students in other majors seeking a minor or concentration. This document provides guidelines for development of curricula for such programs.

Undergraduate programs in statistics are intended to equip students with quantitative skills that they can employ and build on in flexible ways. Some students will plan graduate work in statistics or other fields, while others will seek employment after their first degree. Programs should be sufficiently flexible to accommodate varying goals. Undergraduate programs are not intended to train professional statisticians, though some graduates may reach this level through work experience and/or further study.

Institutions vary greatly in the type and intensity of programs they are able to offer. The ASA believes almost all institutions can provide a level of statistical education that is useful to both students and employers. We encourage flexibility in adapting these guidelines to institutional constraints. In many cases, statistics minors or concentrations for quantitatively oriented students in fields such as biology, business, and behavioral and social science may be more feasible than a full statistics major.

Undergraduate statistics programs should emphasize concepts and tools for working with data and provide experience in designing data collection and analyzing real data that go beyond the content of a first course in statistical methods. The detailed statistical content may vary, and may be accompanied by varying levels of study in computing, mathematics, and a field of application.

Though statistics requires mathematics for the development of its underlying theory, statistics is distinct from mathematics and uses many nonmathematical skills; thus, the curriculum must be more than a sequence of mathematics courses. It is essential that faculty trained in statistics and experienced in working with data be involved in developing statistics programs and teaching or supervising courses required by the programs.

Comments on the curriculum guiding principles

Skills Needed
Effective statisticians at any level display a combination of skills that are not exclusively mathematical. Programs should provide some background in the following areas:

Statistical - Graduates should have training and experience in statistical reasoning, in designing studies (including practical aspects), in exploratory analysis of data by graphical and other means, and in a variety of formal inference procedures.

Mathematical - Undergraduate major programs should include study of probability and statistical theory, along with the prerequisite mathematics, especially calculus and linear algebra. Programs for nonmajors may require less study of mathematics. Programs preparing for graduate work may require additional mathematics.

Computational - Working with data requires more than basic computing skills. Programs should require familiarity with a standard statistical software package and encourage study of data management and algorithmic problemsolving.

Nonmathematical - Graduates should be expected to write clearly, speak fluently, and have developed skills in collaboration and teamwork and organizing and managing projects. Academic programs often fail to offer adequate preparation in these areas.

Substantive area - Because statistics is a methodological discipline, statistics programs should include some depth in an area of application.

Comments on the skills needed

Curriculum Topics for Undergraduate Degrees in Statistical Science
The approach to teaching the following topics should:
• Emphasize real data and authentic applications
• Present data in a context that is both meaningful to students and indicative of the science behind the data
• Include experience with statistical computing
• Encourage synthesis of theory, methods, and applications
• Offer frequent opportunities to develop communication skills

Statistical Topics
• Statistical theory (e.g., distributions of random variables, point and interval estimation, hypothesis testing, Bayesian methods)
• Graphical data analysis methods
• Statistical modeling (e.g., simple, multiple, and logistic regression; categorical data; diagnostics; data mining)
• Design of studies (e.g., random assignment, replication, blocking, analysis of variance, fixed and random effects, diagnostics in experiments; random sampling, stratification in sample surveys; data exploration in observational studies)

Mathematical Topics
• Calculus (integration and differentiation) through multivariable calculus
• Applied linear algebra (emphasis on matrix manipulations, linear transformations, projections in Euclidean space, eigenvalue/eigenvector decomposition and singular-value decomposition)

• Emphasis on connections between concepts and their applications in statistics

Computational Topics
• Programming concepts; database concepts and technology
• Professional statistical software appropriate for a variety of tasks

Nonmathematical Topics
• Effective technical writing and presentations
• Teamwork and collaboration
• Planning for data collection
• Data management

Electives - There are many electives that might be included in a statistics major. As resources will vary among institutions, the identification of what will be offered is left to the discretion of individual units.

Practice - When possible, the undergraduate experience should include an internship, senior-level "capstone" course, consulting experience, or a combination of these. These and other opportunities to practice statistics should be included in a variety of venues in an undergraduate program.

Comments on the curriculum topics

Curriculum Topics for Minors or Concentrations in Statistical Science
The core of a minor or concentration in statistics should consist of the following:
• General statistical methodology (statistical thinking, descriptive, estimation, testing, etc.)
• Statistical modeling (simple and multiple regression, diagnostics, etc.)
• Exposure to professional statistical software

The number of credit hours for minors or concentrations will depend on the policies set by the academic units involved. Additional topics to complete the required number of credit hours could be chosen from some nonexhaustive list (e.g., mathematical statistics, design of experiments, categorical data analysis, time series, Bayesian methods, probability, database management, a capstone experience). Courses from other departments with significant statistical content might be allowed to count toward a statistics minor or concentration, though the content of such courses must differ substantially from the others.

Comments on the curriculum topics for minors or concentrations in statistical science

What other resources could be provided in addition to the guidelines that would be helpful to departments?

Please provide any additional comments, recommendations, or resources regarding updating the undergraduate curriculum guidelines.