Bioinformatics vs Biostatistics: Choosing the Right Career Path

Bioinformatics and biostatistics are distinct quantitative disciplines that support modern biomedical research, clinical practice, and public health. Bioinformatics focuses on developing and applying computational methods to analyze biological data such as genomes, proteins, and metabolic pathways. Biostatistics focuses on designing studies, analyzing health data, and interpreting patterns of disease and treatment effects in populations. This article provides a decision framework comparing education, daily tasks, salary, and job outlook to help students, researchers, and life-science professionals choose between these two career paths.

At a Glance

The table below summarizes the primary differences between bioinformatics and biostatistics across key career dimensions. Use it as a starting point for your decision, then read the detailed sections that follow.

Defining the Two Fields

Bioinformatics sits at the intersection of biology, computer science, and information technology. Professionals in this field collect, store, analyze, and present complex biological data. Their work contributes to basic biology, disease detection, drug design, modeling biosystems, forensics, agriculture, and environmental sciences through the combination of biological analysis and high-performance computing. The Bioinformatics and Computational Biology degree description from 2023 emphasizes that specialists in this field combine a solid foundation in biotechnology with the computational skills required for bioinformatics.

Biostatistics sits at the intersection of statistics, mathematics, and health sciences. Biostatisticians apply statistical theory and methods to answer questions about human health, disease patterns, treatment effectiveness, and risk factors. They design experiments and observational studies, determine appropriate sample sizes, analyze data using regression models and survival analysis, and communicate findings to clinical and public health audiences.

The two fields overlap in some areas, particularly in the analysis of large health datasets. A bioinformatician analyzing gene expression data and a biostatistician analyzing clinical trial outcomes both use programming and statistical methods. However, their training priorities, daily responsibilities, and career trajectories differ in meaningful ways.

Educational Pathways and Prerequisites

Bioinformatics Education

Graduate programs in bioinformatics and computational biology typically require coursework in computer science, statistics, molecular biology, and genetics. Students learn to develop and use bioinformatics software, manage biological databases, and apply machine learning methods to biological problems. The Bioinformatics and Computational Biology program description notes that the flexibility of the degree structure permits students to custom design their curriculum under an advisor's guidance, making the program especially relevant for students employed in current diverse biotechnology workplace.

Undergraduate preparation for bioinformatics usually includes computer science courses in programming, data structures, and algorithms, along with biology courses in genetics, molecular biology, and biochemistry. Mathematics coursework through calculus and linear algebra is typically required. Some programs also expect introductory statistics.

Graduate training in bioinformatics can take the form of a master's degree, a PhD, or a professional certificate. Master's programs typically take two years and prepare students for industry roles or further doctoral study. PhD programs take four to six years and emphasize original research, often involving the development of new computational methods or the analysis of large biological datasets.

Biostatistics Education

Biostatistics training emphasizes mathematical statistics, probability theory, experimental design, and applied regression methods. Graduate programs typically require coursework in statistical theory, categorical data analysis, survival analysis, longitudinal data analysis, and clinical trial methodology. Students also take courses in epidemiology and public health to understand the context in which health data are generated.

Undergraduate preparation for biostatistics usually includes mathematics through calculus and linear algebra, a strong foundation in probability and statistical inference, and coursework in biology or health sciences. Programming skills in R, SAS, or Python are increasingly expected.

Master of Science programs in biostatistics typically take two years and prepare graduates for positions in pharmaceutical companies, academic medical centers, government agencies, and contract research organizations. PhD programs take four to five years and prepare graduates for independent research careers in academia, government, or industry.

Continuing Education and Training Programs

Both fields offer structured training opportunities beyond formal degree programs. The National Institutes of Health Office of Intramural Training and Education provides training programs for students and postdoctoral fellows interested in biomedical research careers, including computational and statistical approaches. These programs offer hands-on experience in research settings and can help early-career professionals build skills and networks.

Specialized training programs also exist for researchers in specific regions or disciplines. The Growing Data science Research in Africa to Stimulate Progress (GRASP) program described in a 2025 article is a three-year program designed to build sustainable data science research capacity among qualified African scholars. The program includes a nine-month postgraduate certificate with virtual training, a six-week residency, networking sessions, and mentored research projects. Programs like this demonstrate the growing demand for data science skills in health research across global settings.

Daily Tasks and Work Environments

What Bioinformaticians Do

Bioinformaticians spend much of their time writing and running computational pipelines that process biological data. A typical day might involve cleaning sequencing data, aligning reads to a reference genome, identifying genetic variants, or analyzing protein structures. They build databases, develop software tools, and create visualizations that help biologists interpret complex data.

The work often requires close collaboration with laboratory scientists. A bioinformatician might analyze data generated by a genomics core facility, interpret results for a research team studying a specific disease, or develop new algorithms to address emerging analytical challenges. The Bioinformatics and Computational Biology program description notes that graduates are qualified to pursue careers that require knowledge of current bioinformatics methods and applications, and the ability to develop and use new bioinformatics software.

Bioinformaticians work in diverse settings. Academic research labs employ them to support genomics, transcriptomics, and proteomics projects. Biotechnology and pharmaceutical companies hire them to analyze data from drug discovery programs, develop biomarkers, and support precision medicine initiatives. Genomic sequencing centers and core facilities employ bioinformaticians to process and analyze large volumes of sequencing data.

What Biostatisticians Do

Biostatisticians design studies and analyze data to answer health-related questions. A typical day might involve calculating sample sizes for a proposed clinical trial, writing a statistical analysis plan, running regression models on electronic health record data, or preparing tables and figures for a manuscript or regulatory submission.

Biostatisticians work closely with clinicians, epidemiologists, and public health researchers. They help define research questions, identify appropriate data sources, determine analytical approaches, and interpret results in the context of the study design. They also play a critical role in ensuring that studies are designed with sufficient power to detect meaningful effects and that analyses account for potential biases and confounding factors.

Biostatisticians are employed in academic medical centers, schools of public health, government agencies such as the Centers for Disease Control and Prevention and the Food and Drug Administration, pharmaceutical companies, and contract research organizations. They also work in health insurance, health technology assessment, and health policy organizations.

Comparing Daily Work

The daily work of bioinformaticians and biostatisticians differs in the nature of the data and the analytical approaches used. Bioinformaticians typically work with molecular data such as DNA sequences, RNA expression levels, protein structures, and metabolite concentrations. Their analyses often involve large, high-dimensional datasets that require specialized computational infrastructure and algorithms.

Biostatisticians typically work with health data such as clinical measurements, survey responses, electronic health records, and disease registries. Their analyses often involve smaller, structured datasets that require careful attention to study design, sampling, and statistical inference.

Both fields require strong programming skills, but the emphasis differs. Bioinformaticians often use Python, R, and specialized bioinformatics tools such as alignment software and genome browsers. Biostatisticians often use R, SAS, and Stata, with increasing use of Python for machine learning applications.

Core Skills and Competencies

Programming and Computational Skills

Bioinformatics requires proficiency in programming languages commonly used for biological data analysis. Python and R are the most widely used, with additional tools such as Bash for command-line operations and SQL for database management. Bioinformaticians must be comfortable working in high-performance computing environments and managing large datasets that do not fit in memory.

Biostatisticians also need strong programming skills, particularly in R and SAS. R is widely used for statistical modeling, data visualization, and reproducible research. SAS remains common in regulatory and pharmaceutical settings. Python is increasingly used for machine learning and working with large datasets.

Statistical and Mathematical Skills

Biostatisticians need a deep understanding of statistical theory, including probability distributions, hypothesis testing, confidence intervals, and regression models. They must understand the assumptions underlying different statistical methods and know how to diagnose and address violations of those assumptions.

Bioinformaticians need sufficient statistical knowledge to analyze biological data correctly, but their training typically emphasizes computational methods more heavily than statistical theory. They must understand concepts such as multiple testing correction, false discovery rates, and cross-validation, but they may not need the same depth of mathematical statistics as biostatisticians.

Domain Knowledge

Bioinformaticians need a working knowledge of molecular biology, genetics, and genomics. They must understand what biological data represent and how to interpret analytical results in biological context. This domain knowledge is essential for making meaningful contributions to research projects.

Biostatisticians need a working knowledge of clinical medicine, epidemiology, and public health. They must understand how health data are generated, what questions are clinically meaningful, and how to communicate results to clinical audiences.

Career Opportunities and Job Outlook

Bioinformatics Career Paths

Bioinformatics careers span academic, industry, and government settings. In academia, bioinformaticians work as research staff, core facility directors, and faculty members. In industry, they work in genomics companies, pharmaceutical R&D, biotechnology startups, and agricultural biotechnology. Government agencies employ bioinformaticians for research, public health surveillance, and regulatory science.

The U.S. Bureau of Labor Statistics Life, Physical, and Social Science Occupations page provides information about careers that apply scientific knowledge to solve problems in health, agriculture, and the environment. Bioinformatics positions often fall within broader occupational categories such as computer and information research scientists, biological technicians, or medical scientists, depending on the specific role and employer.

Career progression in bioinformatics often follows a path from research assistant or analyst to senior bioinformatician, principal scientist, or director of bioinformatics. Some professionals move into software development, data engineering, or product management roles in biotechnology companies.

Biostatistics Career Paths

Biostatistics careers are concentrated in health research and healthcare. Academic medical centers and schools of public health employ biostatisticians to support clinical research and teach. Pharmaceutical companies and contract research organizations employ them to design and analyze clinical trials. Government agencies employ them for disease surveillance, health policy analysis, and regulatory review.

The U.S. Bureau of Labor Statistics Healthcare Occupations page provides information about careers in healthcare settings. Biostatisticians often work alongside healthcare professionals in hospitals, research institutes, and public health agencies, supporting research that informs patient care and health policy.

Career progression in biostatistics often follows a path from statistical analyst to biostatistician, senior biostatistician, principal biostatistician, or director of biostatistics. Some professionals move into leadership roles in clinical research organizations, regulatory agencies, or health technology companies.

Job Market Considerations

Both fields have strong job prospects, but the specific opportunities differ. Bioinformatics has seen rapid growth driven by advances in DNA sequencing, precision medicine, and the increasing availability of large biological datasets. The NCBI Literature Resources and PubMed databases demonstrate the rapid growth of published research in genomics and computational biology, reflecting the expanding scope of the field.

Biostatistics has steady demand driven by the ongoing need for rigorous evaluation of medical treatments, public health interventions, and health policies. Clinical trials, epidemiological studies, and health services research all require biostatistical expertise. The O*NET OnLine database, maintained by the U.S. Department of Labor, provides detailed information about the tasks, skills, and work contexts of occupations in both fields.

Salary Considerations

Salary data for bioinformatics and biostatistics positions vary by education level, experience, geographic location, and employment sector. The U.S. Bureau of Labor Statistics Life, Physical, and Social Science Occupations page and the U.S. Bureau of Labor Statistics Healthcare Occupations page provide occupational employment and wage information for relevant categories.

In general, positions in the pharmaceutical and biotechnology industries tend to pay more than academic positions at equivalent experience levels. Positions in major metropolitan areas and biotechnology hubs tend to pay more than positions in smaller cities or rural areas. Advanced degrees typically lead to higher salaries and more rapid career advancement.

Bioinformaticians with strong programming skills and experience with machine learning may command premium salaries in the biotechnology and technology sectors. Biostatisticians with experience in clinical trial design and regulatory submissions may command premium salaries in the pharmaceutical and contract research organization sectors.

Decision Framework for Choosing Between the Fields

Assess Your Interests and Strengths

Start by assessing your interests and strengths across the following dimensions:

Interest in biology versus health. Do you find molecular biology, genetics, and genomics more compelling, or are you more drawn to clinical medicine, epidemiology, and public health? Bioinformatics is the better fit if you are fascinated by the molecular mechanisms of life. Biostatistics is the better fit if you are drawn to understanding disease patterns and treatment effects in populations.

Comfort with programming versus statistics. Do you enjoy writing code, building pipelines, and working with large datasets, or do you prefer mathematical reasoning, study design, and statistical inference? Bioinformatics requires substantial programming skill. Biostatistics requires substantial statistical theory and applied modeling skill.

Preference for data types. Do you want to work with sequencing data, protein structures, and gene expression profiles, or with clinical measurements, survey data, and electronic health records? The nature of the data you work with will shape your daily experience in each field.

Career setting preferences. Do you see yourself in a genomics lab, a biotechnology company, or a clinical research center? Consider the work environments that appeal to you and the types of colleagues you want to collaborate with.

Evaluate Educational Requirements

Consider the educational path required for each field and whether it aligns with your circumstances and goals. Both fields typically require graduate education for meaningful career advancement. A master's degree is often sufficient for applied positions in industry or academic research support. A PhD is typically required for independent research careers, faculty positions, and senior leadership roles.

If you are considering a career change from a related field, assess the prerequisites for graduate programs in each discipline. Bioinformatics programs may require coursework in computer science and biology. Biostatistics programs may require coursework in mathematics and statistics. Some programs offer preparatory coursework or certificate options for students with gaps in their background.

Consider Long-Term Career Trajectories

Think about where you want to be in ten to fifteen years. Bioinformatics offers opportunities to work at the cutting edge of genomics and precision medicine, with new technologies and analytical challenges emerging regularly. Biostatistics offers opportunities to contribute to the evidence base for medical treatments and public health policies, with a stable and well-established career structure.

Both fields offer opportunities for leadership, teaching, and entrepreneurship. Bioinformaticians may found companies that develop analytical software or provide genomic services. Biostatisticians may lead biostatistics cores, direct clinical trial programs, or advise health policy makers.

Test the Waters

Before committing to a graduate program or career path, seek opportunities to gain hands-on experience in each field. Take introductory courses in bioinformatics and biostatistics. Attend seminars and conferences. Talk to professionals in each field about their daily work and career satisfaction. Consider internships or research assistant positions that expose you to the actual work of each discipline.

The National Institutes of Health Office of Intramural Training and Education offers training opportunities that can help you explore biomedical research careers. The O*NET OnLine database provides detailed occupational information that can help you understand the day-to-day tasks and work contexts of specific jobs.

Practical Implementation Steps

Step 1: Conduct a Self-Assessment

Write down your answers to the following questions. Be specific and honest about your preferences and constraints.

• What courses have you enjoyed most in your academic career?
• What skills do you want to use in your daily work?
• What kind of colleagues and work environment do you prefer?
• What are your financial and geographic constraints?
• What are your long-term career goals?

Step 2: Research Graduate Programs

Identify graduate programs in bioinformatics and biostatistics that match your interests and qualifications. Review their curricula, faculty research areas, and placement records. Contact current students and alumni to learn about their experiences.

Step 3: Compare Job Postings

Review current job postings for bioinformatics and biostatistics positions. Note the required qualifications, preferred skills, and salary ranges. This will give you a realistic picture of the job market for each field.

Step 4: Seek Informational Interviews

Reach out to professionals in each field for informational interviews. Ask about their daily work, career progression, and advice for someone considering their field. The O*NET OnLine database can help you identify the specific occupations and job titles to research.

Step 5: Make a Decision and Commit

After completing your research, make a decision based on your interests, strengths, and career goals. Commit to the educational path and professional development activities that will position you for success in your chosen field.

Records and Measurements for Career Planning

Track Your Skills Development

Maintain a record of the skills you develop through coursework, research, and work experience. For bioinformatics, track your proficiency in programming languages, bioinformatics tools, and database management. For biostatistics, track your proficiency in statistical software, study design, and analytical methods.

Document Your Projects

Keep a portfolio of projects that demonstrate your skills and experience. For bioinformatics, this might include pipelines you have built, analyses you have conducted, or software you have developed. For biostatistics, this might include study protocols you have designed, analyses you have conducted, or reports you have written.

Monitor the Job Market

Regularly review job postings and salary data for positions in your chosen field. The U.S. Bureau of Labor Statistics Life, Physical, and Social Science Occupations page and the U.S. Bureau of Labor Statistics Healthcare Occupations page provide occupational outlook information. The O*NET OnLine database provides detailed information about specific occupations.

Evaluate Your Progress Annually

Set annual goals for your career development and evaluate your progress against those goals. Identify skill gaps and seek training or experience to address them. Adjust your career plan as your interests and circumstances evolve.

Common Failure Patterns in Career Decision Making

Choosing Based on Salary Alone

Salary is an important consideration, but it should not be the sole basis for choosing between bioinformatics and biostatistics. Both fields offer competitive salaries, but the daily work differs substantially. If you choose a field that does not match your interests and strengths, you are unlikely to perform at your best or find long-term satisfaction.

Underestimating the Mathematical Requirements

Both fields require substantial mathematical and statistical training. Students who struggle with mathematics may find either field challenging. Before committing to a graduate program, assess your mathematical preparation and willingness to develop these skills.

Overlooking the Importance of Domain Knowledge

Bioinformatics requires biological knowledge, and biostatistics requires health and medical knowledge. Students who focus exclusively on computational or statistical skills without developing domain knowledge may struggle to collaborate effectively with scientists and clinicians.

Ignoring the Reality of Daily Work

The daily work of bioinformatics and biostatistics can differ from the impression created by course descriptions or popular media. Before committing to a career path, seek hands-on experience and talk to professionals about the realities of their work.

Failing to Plan for Graduate Education

Both fields typically require graduate education for meaningful career advancement. Students who fail to plan for the financial and time costs of graduate education may face unexpected challenges. Research funding opportunities, assistantships, and fellowship programs early in your planning process.

Limitations and Considerations

Geographic Variation in Job Markets

Job opportunities and salaries for bioinformatics and biostatistics vary by geographic region. Biotechnology hubs such as Boston, San Francisco, and San Diego have strong demand for bioinformaticians. Academic medical centers and government agencies in many regions employ biostatisticians. Consider your geographic preferences and constraints when evaluating career options.

Sector Differences

Employment conditions differ across academic, industry, and government sectors. Academic positions may offer more intellectual freedom but lower salaries and less job security. Industry positions may offer higher salaries but more demanding timelines and proprietary constraints. Government positions may offer stability and benefits but limited advancement opportunities.

Evolving Skill Requirements

Both fields are evolving rapidly. Bioinformatics is being transformed by advances in machine learning, single-cell technologies, and long-read sequencing. Biostatistics is being transformed by the increasing availability of electronic health records, real-world evidence, and Bayesian methods. Professionals in both fields must commit to lifelong learning to remain current.

Interdisciplinary Nature

Both fields are inherently interdisciplinary. Success requires also technical skills but also the ability to communicate with scientists, clinicians, and policymakers. Professionals who develop strong communication and collaboration skills will be better positioned for career advancement.

Professional Escalation Criteria

When to Seek Additional Training

Consider seeking additional training or education if you find that your current skills are insufficient for the work you want to do. Signs that you may need additional training include difficulty securing positions you are qualified for, frequent rejection of your analytical work by collaborators, or a sense that your technical skills are outdated.

When to Consult a Career Advisor

Consider consulting a career advisor if you are uncertain about your career direction, struggling to find positions that match your qualifications, or considering a significant career change. Career advisors can help you assess your options, refine your job search strategy, and develop a plan for professional development.

When to Seek Mentorship

Seek mentorship from experienced professionals in your chosen field. Mentors can provide guidance on career decisions, introduce you to professional networks, and help you navigate challenges. The National Institutes of Health Office of Intramural Training and Education offers mentoring and career development resources for biomedical researchers.

When to Reassess Your Career Path

Periodically reassess your career path to ensure it remains aligned with your interests, values, and goals. Signs that you may need to reassess include persistent dissatisfaction with your work, a sense that your skills are underutilized, or a change in your personal circumstances that affects your career priorities.

Frequently Asked Questions

What is the main difference between bioinformatics and biostatistics?

Bioinformatics focuses on computational analysis of biological data such as genomes, proteins, and gene expression profiles. Biostatistics focuses on statistical design and analysis of health studies, clinical trials, and population health data. Bioinformatics requires stronger programming and molecular biology skills, while biostatistics requires stronger mathematical statistics and study design skills.

Which field has better job prospects?

Both fields have strong job prospects, but the specific opportunities differ. Bioinformatics has seen rapid growth driven by advances in DNA sequencing and precision medicine. Biostatistics has steady demand driven by the ongoing need for rigorous evaluation of medical treatments and public health interventions. The U.S. Bureau of Labor Statistics Life, Physical, and Social Science Occupations page and the U.S. Bureau of Labor Statistics Healthcare Occupations page provide occupational outlook information for relevant categories.

Can I switch from bioinformatics to biostatistics or vice versa?

Yes, it is possible to switch between the fields, but it typically requires additional education or training. The core skills overlap in programming and data analysis, but each field has specialized knowledge that must be acquired. Consider taking coursework or seeking mentorship in the new field before making a complete transition.

Do I need a PhD to work in bioinformatics or biostatistics?

A PhD is not always required, but it is often necessary for independent research careers, faculty positions, and senior leadership roles. A master's degree is often sufficient for applied positions in industry or academic research support. The specific requirements depend on the employer and the nature of the position.

What programming languages should I learn?

For bioinformatics, Python and R are the most widely used programming languages. For biostatistics, R and SAS are the most widely used, with Python increasingly important for machine learning applications. Learning both R and Python will serve you well in either field.

How do salaries compare between bioinformatics and biostatistics?

Salaries vary by education level, experience, geographic location, and employment sector. Positions in the pharmaceutical and biotechnology industries tend to pay more than academic positions. The U.S. Bureau of Labor Statistics Life, Physical, and Social Science Occupations page and the U.S. Bureau of Labor Statistics Healthcare Occupations page provide wage information for relevant occupational categories.

What kind of work environments can I expect?

Bioinformaticians work in academic research labs, biotechnology companies, pharmaceutical R&D, and genomic sequencing centers. Biostatisticians work in academic medical centers, government health agencies, pharmaceutical companies, and contract research organizations. Both fields offer opportunities for remote work and hybrid arrangements, though the extent varies by employer.

How do I decide which field is right for me?

Assess your interests in biology versus health, your comfort with programming versus statistics, and your preferences for data types and work environments. Research graduate programs and job postings in both fields. Seek informational interviews with professionals in each field. Gain hands-on experience through coursework, internships, or research positions before committing to a career path.

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References and Further Reading

• Life, Physical, and Social Science Occupations. U.S. Bureau of Labor Statistics.
• Healthcare Occupations. U.S. Bureau of Labor Statistics.
• O*NET OnLine. U.S. Department of Labor.
• Office of Intramural Training and Education. National Institutes of Health.
• NCBI Literature Resources. National Center for Biotechnology Information.
• PubMed. National Library of Medicine.
• Incidence, prevalence, and co-occurrence of autoimmune disorders over time and by age, sex, and socioeconomic status: a population-based cohort study of 22 million individuals in the UK.. Lancet (London, England), 2023.
• Tumor-derived GDF-15 blocks LFA-1 dependent T cell recruitment and suppresses responses to anti-PD-1 treatment.. Nature communications, 2023.
• Human plasma proteomic profile of clonal hematopoiesis.. Nature communications, 2025.
• Global burden of 292 causes of death in 204 countries and territories and 660 subnational locations, 1990-2023: a systematic analysis for the Global Burden of Disease Study 2023.. Lancet (London, England), 2025.
• Gut microbiome compositional and functional features associate with Alzheimer's disease pathology.. Alzheimer's & dementia : the journal of the Alzheimer's Association, 2025.
• Global burden of enteric infectious diseases, diarrhoeal diseases, and corresponding aetiologies, 1990-2023: a systematic analysis for the Global Burden of Disease Study 2023.. The Lancet. Infectious diseases, 2026.
• Comparing Durvalumab, Olaparib, and Cediranib Monotherapy, Combination Therapy, or Chemotherapy in Patients with Platinum-Resistant Ovarian Cancer with Prior Bevacizumab: The Phase II NRG-GY023 Trial.. Clinical cancer research : an official journal of the American Association for Cancer Research, 2025.
• Association of Perceived Neighborhood Health With Hypertension Self-care.. JAMA network open, 2023.
• Predicting pulmonary hypertension in infants with bronchopulmonary dysplasia.. 2026.
• Microcosting the optimization of PrEP implementation among sexual and gender minority individuals with a substance use disorder.. 2026.
• Extensive diversity and impact of drug-resistant HIV-1 variants in individuals with prior virologic failure.. 2026.
• Growing Data science Research in Africa to Stimulate Progress (GRASP) program: Rationale and overview.. 2025.
• DECODING CAREERS IN DNA: Genetic Coding Lesson Brings Computational Biology and STEM Careers to Life. 2018.
• Bioinformatics and Computational Biology. 2023.
• International Society for Computational Biology Honors Dana Pe'er with Top Bioinformatics/Computational Biology Award for 2014. PLoS Comput. Biol., 2014.
• Webb Miller and Trey Ideker To Receive Top International Bioinformatics Awards for 2009 from the International Society for Computational Biology. PLoS Comput. Biol., 2009.

This article is educational and does not replace institutional policy, professional advice, or applicable safety and regulatory requirements.