What is Experimental Design?
Experimental design is the process of researching in an objective and controlled manner to maximize precision and draw specific conclusions about a hypothesis statement. It is a concept used to efficiently organize, conduct, and interpret the results of experiments to ensure that as much useful information as possible is obtained by conducting a small number of trials. This minimizes the effects of the variables to increase the reliability of the results.
How is Experimental Design used?
Zippia reviewed thousands of resumes to understand how experimental design is used in different jobs. Explore the list of common job responsibilities related to experimental design below:
- Advised drug sponsors on experimental design, sample size and model validity.
- Supported the non-clinical and clinical scientists with experimental design and statistical analysis and provided the consultation and resolution of business issues.
- Participated in experimental design with power analysis of pilot study, and communicated the key analysis results to the Primary Investigators.
- Constructed R packages to deliver statistical estimates for analysis of data from experimental designs common in agricultural research.
- Participated extensively in the hypothesis generation, experimental design, protocol planning and study organization process.
- Develop, analyze and evaluate proposed modeling with regards to experimental design and statistical analysis.
Are Experimental Design skills in demand?
Yes, experimental design skills are in demand today. Currently, 2,066 job openings list experimental design skills as a requirement. The job descriptions that most frequently include experimental design skills are mathematical statistician, junior scientist, and senior research associate scientist.
How hard is it to learn Experimental Design?
Based on the average complexity level of the jobs that use experimental design the most: mathematical statistician, junior scientist, and senior research associate scientist. The complexity level of these jobs is challenging.
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What jobs can you get with Experimental Design skills?
You can get a job as a mathematical statistician, junior scientist, and senior research associate scientist with experimental design skills. After analyzing resumes and job postings, we identified these as the most common job titles for candidates with experimental design skills.
Mathematical Statistician
Job description:
Mathematical statisticians apply mathematical and statistical techniques to solve and analyze data. It is part of their responsibility to prepare reports that communicate their analyses and results. As statisticians, they collect data, design survey materials and other instruments for gathering data, and manage on-line databases of information. They work by determining what problems their analyses need to decide then select what appropriate methods for data gathering and analysis. Moreover, the data they gathered is analyzed, and they also interpret the results, draw conclusions, and make reports for use by decision-makers.
- Statistical Methods
- Experimental Design
- Statistical Models
- Sample Size
- Statistical Research
- Survey Design
Junior Scientist
Job description:
A junior scientist is in charge of conducting research and scientific studies while under the supervision of a more experienced scientist. Their responsibilities typically revolve around gathering and preparing samples, performing experiments and analysis, coordinating with other experts, recording all progress, reviewing results, and summarizing findings into reports and presentations. In a company setting, a junior scientist must adhere to deadlines and budgets, submitting results to senior scientists and managers. Furthermore, it is essential to uphold the policies and regulations of laboratories to maintain a safe and productive work environment.
- Lab Equipment
- Literature
- Data Analysis
- GMP
- Experimental Design
- Cell Culture Techniques
Senior Research Associate Scientist
Job description:
Senior research associate scientists serve a critical function assisting in the development of research. The senior research associate scientists support ongoing studies that have something to do with biometric identification, anti-drug or antibody detection, and cell functions, especially in our immune system. They should adjust well to fast-paced environments and apply their knowledge to various projects and experiments. They should be adept in doing cell-based experiments, techniques on a molecular level, and flow cytometry. Being detail-oriented and having strong communication skills can also help them become efficient in this field.
- Cell Culture
- Cell-Based Assays
- Data Analysis
- Elisa
- Experimental Design
- Assay Development
Product Development Scientist
Job description:
A product development scientist is responsible for conducting in-depth scientific research and method analysis to develop medical technologies, medications, and foods, depending on the industry. Product development scientists may also perform enhancements on existing products by studying its components and improve its features. They evaluate the manufacturing processes of a product, providing recommendations on additional resources to generate revenues and profits. A product development scientist often works in a laboratory, requiring them to follow strict safety protocols and ensuring the cleanliness and orderliness of the area to prevent contamination and result inconsistencies.
- Chemistry
- Project Management
- Data Analysis
- FDA
- Experimental Design
- GMP
Principal Scientist
Job description:
A Principal Scientist is focused on leading scientific teams and conducting research. They ensure that their teams have the resources to properly perform research tasks.
- Oncology
- Data Analysis
- Drug Discovery
- GMP
- Experimental Design
- Clinical Trials
Scientific Consultant
Job description:
A scientific consultant provides consultation services for scientific projects and research for implementation and enabling scientific missions to the customers. They strategize solutions for business or organizational problems as well as providing a fresh perspective and knowledge based on their expertise. Their duties and responsibilities include compiling and presenting information to the organization through reports.
- SQL
- R
- FDA
- Laboratory Procedures
- Experimental Design
- Scientific Journals
How much can you earn with Experimental Design skills?
You can earn up to $62,612 a year with experimental design skills if you become a mathematical statistician, the highest-paying job that requires experimental design skills. Junior scientists can earn the second-highest salary among jobs that use Python, $63,169 a year.
| Job title | Average salary | Hourly rate |
|---|---|---|
| Mathematical Statistician | $62,612 | $30 |
| Junior Scientist | $63,169 | $30 |
| Senior Research Associate Scientist | $72,094 | $35 |
| Product Development Scientist | $84,817 | $41 |
| Principal Scientist | $119,982 | $58 |
Companies using Experimental Design in 2025
The top companies that look for employees with experimental design skills are Intel, Stanford University, and The Ohio State University. In the millions of job postings we reviewed, these companies mention experimental design skills most frequently.
| Rank | Company | % of all skills | Job openings |
|---|---|---|---|
| 1 | Intel | 30% | 367 |
| 2 | Stanford University | 6% | 892 |
| 3 | The Ohio State University | 6% | 460 |
| 4 | Meta | 5% | 10,857 |
| 5 | Merck | 5% | 2,000 |
2 courses for Experimental Design skills
1. Experimental Design & Recommendations
Learn two of the most in-demand skills in the entire field of data science! By the end of this course, you’ll know how to generate personalized recommendations based on user data, as well as run statistically valid tests that produce clean, interpretable results...
2. ANOVA and Experimental Design
This second course in statistical modeling will introduce students to the study of the analysis of variance (ANOVA), analysis of covariance (ANCOVA), and experimental design. ANOVA and ANCOVA, presented as a type of linear regression model, will provide the mathematical basis for designing experiments for data science applications. Emphasis will be placed on important design-related concepts, such as randomization, blocking, factorial design, and causality. Some attention will also be given to ethical issues raised in experimentation. This course can be taken for academic credit as part of CU Boulder’s Master of Science in Data Science (MS-DS) degree offered on the Coursera platform. The MS-DS is an interdisciplinary degree that brings together faculty from CU Boulder’s departments of Applied Mathematics, Computer Science, Information Science, and others. With performance-based admissions and no application process, the MS-DS is ideal for individuals with a broad range of undergraduate education and/or professional experience in computer science, information science, mathematics, and statistics. Learn more about the MS-DS program at https://www.coursera.org/degrees/master-of-science-data-science-boulder. Logo adapted from photo by Vincent Ledvina on Unsplash...