Each BBSP student is required to read and agree to our academic standards policy. A summary of the policy is below. Click here for the full version of the policy with explanations for each section.
All BBSP students are expected to:
- Attend Orientation week.
- Complete 3 research rotations.
- Enroll in the BBSP First Year group course (BBSP 902) for both the Fall and Spring semesters.
- Complete the Quantitative Skills/Biostatistics training.
- Enroll in coursework that counts towards one of the BBSP degree programs. The minimum requirement is enrollment in 3 units of course credit/semester (i.e. non-research) in addition to the FYG course and the research rotation.
- Complete Ethics Training.[jump link to Ethics section below)
- Abide by the UNC Honor Code.
- Remain in “Good Academic Standing”.
a. maintain full-time graduate enrollment with a minimum 9 credit hours
b. do not receive a grade of “F”, or receive nine or more credit hours with a grade of “L” throughout your graduate career
c. present your rotation research in the Fall poster session and the Spring mini-symposium
d. satisfactorily identify a thesis mentor and PhD program in a timely fashion
- Apply for NC Residency in a timely manner.
The BBSP program requires all first year students to participate in a six part Research Ethics Training series covering the following topics
- Mentorship and Social Responsibility
- UNC RCR Resources
- Animal and Human Subject Use
- Experimental Design and Statistics
- Authorship and Peer Reivew
- Intellectual Property and Conflict of Interest
The sessions are led by post-doctoral researchers trained in leading ethics case discussions. Completion of these seven sessions and the associated writing assignments satisfies the NIH requirement for training in Responsible Conduct of Research. Students who miss a session during their BBSP year must attend the session in a subsequent year. Students will receive a certificate of completion that they can use as proof of meeting this requirement.
Ethics/RCR blurb for NIH Training grant submissions
The BBSP recognizes the importance of scientist-trainees possessing knowledge and skills to properly use and interpret statistical tests in relation to scientific data. Therefore, quantitative statistical training is a mandatory component of the first year group experience for BBSP students. Over the course of four sessions, students will learn:
- the importance of statistical analysis in their research
- common statistical computations and tests
- tips for selecting the appropriate statistical test
- statistical resources available on campus
These lessons are a combination of didactic and hands-on instruction led by biostatistics graduate students and the curriculum and training are overseen by Joshua Hall in the Office of Graduate Education.
A more in-depth statistics course, Biostatistics for Laboratory Scientists (BIOS 610), is available through the Biostatistics department. The course is led by Eric Bair, PhD.
Course Description: BIOS 610 introduces the basic concepts and methods of statistics with emphasis on applications in the experimental biological sciences. Emphasis is on mastery of basic statistical skills and familiarity with situations in which advanced analytical skills may be needed. The primary focus of the course is on applications in basic science research, and students with primary interests in prospective epidemiological studies should strongly consider BIOS 600 instead. Course objectives include learning to use statistical reasoning to formulate scientific questions in quantitative terms, learning to design and interpret graphical and tabular displays of statistical information, using basic probability models to describe trends and random variation in laboratory data, and using basic statistical models, including tests and confidence intervals, to draw inferences from data. Topics include point and interval estimation, testing, experimental design, linear regression, sample size and power calculations, measurement error, and a selection of the following: logistic regression, principal components analysis, survival analysis, and methods for correction for multiple testing. Introduces and employs the freely available statistical software, R, to explore and analyze data. Emphasis on mastery of basic statistical analysis skills, familiarity with situations in which advanced analytic skills may be needed, and the ability to critically review statistical analysis presented in relevant manuscripts.
For more information about the course you may contact the instructor by email.