The Scientific Method vs. Engineering Design Process: A Comparison

Students, researchers, and life-science professionals frequently encounter two structured approaches to problem solving: the scientific method and the engineering design process. Both frameworks share a common foundation in systematic inquiry, yet they serve different purposes and produce different types of outcomes. The scientific method seeks to generate knowledge by testing hypotheses about how the natural world works. The engineering design process seeks to create solutions by defining problems, generating ideas, and building and testing artifacts that meet specified requirements. This article compares the two approaches step by step, provides a decision guide for choosing between them, and explains how each framework applies in research and applied settings.

Defining the Two Frameworks

The scientific method is a hypothesis-driven approach. A researcher observes a phenomenon, formulates a hypothesis as a potential answer to a question, designs and conducts an experiment to test that hypothesis, and then interprets the results in light of the original question and previous studies. The outcome of the scientific method is a conclusion about whether the evidence supports or refutes the hypothesis. This conclusion contributes to a body of knowledge that other researchers can build upon or challenge.

The engineering design process is a design-driven approach. An engineer or designer identifies a need or problem, defines criteria and constraints, generates multiple possible solutions, selects and develops the most promising option, builds a prototype or model, and tests it against the requirements. The outcome of the engineering design process is a functional solution, product, or system that meets the stated performance requirements. The emphasis falls on creating something that works, not on producing a generalizable explanation of natural phenomena.

The distinction between hypothesis-driven and design-driven work has direct consequences for how practitioners plan their work, what records they keep, and how they judge success. In biomedical research, for example, the scientific method remains the standard for testing whether a treatment works. In clinical treatment design, however, the engineering design process may be more appropriate because the goal is to produce a functional intervention that meets clinical performance requirements, also to compare the pros and cons of different strategies. A review of biomaterial approaches to skin injury control makes this point explicitly: you need to use the engineering design process, not the scientific method, to design a clinical treatment, and the clinical performance requirements are functional, not structural. This distinction matters for anyone working at the interface of research and application.

At a Glance

The table below summarizes the key differences between the scientific method and the engineering design process across several dimensions.

Core Principles of the Scientific Method

Hypothesis Generation and Testing

The scientific method begins with a question derived from observation. A researcher examines a phenomenon, reviews existing literature, and proposes a hypothesis that offers a potential explanation. The hypothesis must be testable, meaning that it can be supported or refuted by empirical evidence collected through a well-designed experiment.

The quality of the hypothesis determines the quality of the entire investigation. A vague or untestable hypothesis cannot be evaluated, no matter how carefully the experiment is conducted. Researchers therefore spend considerable effort refining their questions and ensuring that their hypotheses are specific enough to generate meaningful predictions.

Experimental Design and Controls

Once a hypothesis is established, the researcher designs an experiment that will produce data relevant to the hypothesis. Key considerations include the selection of appropriate comparison groups, the control of confounding variables, the determination of sample size, and the choice of outcome measures. The NC3Rs Experimental Design Assistant provides a structured platform for researchers to plan experiments, identify potential design flaws, and document their decisions. Using such tools helps researchers avoid common pitfalls such as inadequate sample sizes, biased allocation, and poorly defined endpoints.

The importance of rigorous experimental design extends across all fields of empirical research. Systematic reviews and meta-analyses depend on the availability of well-designed studies that can be pooled and compared. When studies are poorly designed or highly heterogeneous, the conclusions that can be drawn from them are limited. A systematic review of strength training versus stretching for improving range of motion, for example, found no differences between the two interventions on range of motion gains, but the authors noted that the studies were highly heterogeneous in terms of design, protocols, and populations, and so further research was warranted. This example illustrates how the quality of individual studies constrains the strength of the conclusions that can be drawn from the collective evidence.

Data Collection and Analysis

Data collection must be systematic, accurate, and documented. Researchers record their observations, measurements, and experimental conditions in sufficient detail that another researcher could replicate the study. The National Institute of Standards and Technology Research Data Framework addresses the infrastructure and practices needed to manage research data throughout its lifecycle, from collection and storage to sharing and preservation. Proper data management supports reproducibility and enables other researchers to verify or extend the findings.

Statistical analysis transforms raw data into evidence that can be interpreted. The choice of statistical methods depends on the study design, the distribution of the data, and the questions being asked. Researchers must be careful to select methods that match their data and to avoid practices that inflate the risk of false conclusions.

Interpretation and Communication

The final step of the scientific method is interpretation. The researcher examines the results, determines whether they support or refute the hypothesis, and considers how the findings relate to previous studies. The interpretation must acknowledge limitations, including potential sources of bias, the generalizability of the findings, and the strength of the evidence.

Communication of results is essential to the scientific enterprise. Researchers publish their findings in peer-reviewed journals, present them at conferences, and deposit their data in repositories. The EQUATOR Network provides reporting guidelines that help authors describe their studies completely and transparently, which in turn allows readers to assess the quality and applicability of the research. The National Center for Biotechnology Information and PubMed serve as primary access points for the biomedical literature, enabling researchers to locate and retrieve relevant studies.

Core Principles of the Engineering Design Process

Problem Definition and Requirements

The engineering design process begins with a problem or need. The designer must understand the context, identify the stakeholders, and define the criteria and constraints that the solution must satisfy. Criteria are the performance requirements that the solution must meet, such as strength, weight, cost, or durability. Constraints are the limitations within which the designer must work, such as available materials, budget, time, or regulatory requirements.

A well-defined problem statement is the foundation of effective design. Ambiguous or incomplete problem definitions lead to solutions that fail to address the real need. Designers often spend significant time at the front end of a project clarifying the problem, consulting with users, and establishing measurable requirements.

Ideation and Concept Generation

With the problem defined, the designer generates multiple possible solutions. This phase emphasizes creativity and breadth. Techniques such as brainstorming, sketching, and analogical thinking help designers explore a wide range of options before committing to a particular approach. The goal is to avoid premature convergence on a single idea and to consider alternatives that might offer unexpected advantages.

The chef-based optimization algorithm, a metaheuristic method inspired by the process of learning cooking skills, illustrates how structured exploration and exploitation can be balanced in problem solving. The algorithm models the stages of the cooking training process to increase the ability of global search in exploration and the ability of local search in exploitation. While this algorithm is designed for optimization problems instead of engineering design per se, it reflects the broader principle that effective problem solving requires both broad exploration of possibilities and focused refinement of promising options.

Prototyping and Testing

A prototype is a working model of the proposed solution. Prototypes can range from simple physical models to fully functional products. The purpose of prototyping is to test whether the design meets the requirements and to identify areas that need improvement. Testing may involve measuring performance, observing behavior under realistic conditions, and gathering feedback from users.

The integration of the engineering design process in STEM education demonstrates the value of hands-on prototyping. A case study involving the design, creation, and programming of a crane control circuit unit used platforms such as Tinkercad and Micro:Bit to guide students through circuit design, programming, simulation, and physical implementation. The project enhanced students understanding of theoretical concepts while providing practical experience with real applications. This example shows how prototyping and testing are central to the learning and application of engineering design.

Iteration and Refinement

Design is inherently iterative. Testing reveals weaknesses, and the designer modifies the design to address them. This cycle of design, build, test, and refine continues until the solution meets the requirements or until the designer determines that the approach is not viable and returns to earlier stages to explore alternatives.

The iterative nature of design distinguishes it from the linear sequence often associated with the scientific method. While scientific investigations may also involve multiple rounds of experimentation, the design process explicitly expects and plans for repeated cycles of refinement. Each iteration produces new information that informs the next round of design decisions.

Comparing the Steps Side by Side

Similarities Between the Two Approaches

Despite their differences, the scientific method and the engineering design process share several important features. Both approaches begin with a clear definition of the problem or question. Both require systematic planning and careful documentation. Both involve testing ideas against evidence, whether that evidence comes from controlled experiments or prototype performance. Both are iterative in practice, with results feeding back into the next round of work.

Both approaches also depend on the quality of the underlying reasoning. A study of cognitive styles and scientific skepticism found that individuals who favored analytical-rational processing were more likely to hold scientific beliefs, while those who favored intuitive-experiential processing were more likely to hold paranormal beliefs. This finding suggests that the effectiveness of both scientific inquiry and engineering design depends on the practitioners willingness to engage in careful, evidence-based reasoning instead of relying on intuition or untested assumptions.

Differences in Goals and Outcomes

The most fundamental difference between the two approaches lies in their goals. The scientific method aims to produce knowledge. The engineering design process aims to produce solutions. A scientific investigation concludes with a statement about what the evidence shows. An engineering project concludes with a product, system, or process that performs a function.

This difference has practical consequences for how work is evaluated. Scientific work is judged by its contribution to understanding, its reproducibility, and its consistency with other evidence. Engineering work is judged by whether it meets the performance requirements, whether it can be manufactured or implemented at scale, and whether it is reliable and safe in use.

Differences in Iteration Patterns

The scientific method and the engineering design process also differ in how iteration occurs. In science, iteration typically involves refining the hypothesis or the experimental design in response to unexpected results. A failed experiment may lead to a revised hypothesis or a better-controlled study. The goal of iteration is to produce a more definitive test of the hypothesis.

In engineering, iteration is built into the design cycle. Each prototype is tested, and the results directly inform the next design iteration. The goal of iteration is to converge on a solution that meets the requirements. Designers expect to go through multiple cycles and plan their time and resources accordingly.

When to Use Each Approach

Choosing the Scientific Method

Use the scientific method when the goal is to understand a phenomenon, test a causal relationship, or contribute to a body of knowledge. The scientific method is appropriate when you have a testable hypothesis and can design a controlled experiment to evaluate it. This approach is standard in biomedical research, where randomized controlled trials are used to determine whether treatments produce clinically significant improvements in outcomes.

The scientific method is also appropriate when the question is about the natural world and the answer will take the form of an explanation or a generalizable finding. For example, a researcher investigating the relationship between the gut microbiome and major depressive disorder would use the scientific method to identify metabolic signatures, test the direction of associations, and evaluate the interplay between the microbiome and metabolome. The outcome would be knowledge about the biological basis of depression, not a specific intervention.

Choosing the Engineering Design Process

Use the engineering design process when the goal is to create a solution to a practical problem. The engineering design process is appropriate when you have a defined need, a set of requirements, and the intention to produce a functional artifact. This approach is standard in product development, infrastructure design, and clinical device development.

The engineering design process is also appropriate when the problem is open-ended and the solution space is large. Designers must explore multiple options, make tradeoffs, and converge on a solution that balances competing requirements. The process provides a structured way to manage this complexity and to ensure that the final solution meets the needs of the users.

Hybrid Approaches

In practice, many projects combine elements of both approaches. A research and development project may begin with scientific investigation to understand the underlying principles and then shift to engineering design to develop a practical application. Conversely, an engineering project may generate questions that require scientific investigation to answer.

The relationship between the two approaches is complementary instead of competitive. Scientific knowledge informs design decisions by providing an understanding of how materials, systems, and processes behave. Engineering design provides the tools and methods for translating that knowledge into functional solutions. A review of biomaterial approaches to controlling the adaptive response to skin injury illustrates this complementarity: the review examines the normal adaptive response in adults, why it does not meet the desired clinical outcome in most cases, and how biomaterials can be used to alter the response to meet clinical performance requirements. The scientific understanding of wound healing informs the design of biomaterial interventions.

Practical Workflow for the Scientific Method

Step 1: Formulate a Testable Question

Begin by identifying a phenomenon that you want to understand. Review the existing literature to determine what is already known and what gaps remain. The National Center for Biotechnology Information and PubMed provide access to the biomedical literature, allowing you to search for relevant studies and assess the current state of knowledge.

Step 2: Develop a Hypothesis

Based on your review of the literature and your own observations, propose a hypothesis that offers a potential explanation for the phenomenon. Ensure that the hypothesis is specific, testable, and falsifiable. A hypothesis that cannot be tested or that is consistent with any possible outcome is not useful.

Step 3: Design the Experiment

Plan an experiment that will produce data relevant to the hypothesis. Consider the following elements:

• Define the population or system you are studying.
• Identify the variables you will manipulate and measure.
• Determine the sample size needed to detect a meaningful effect.
• Include appropriate control groups or conditions.
• Specify the statistical methods you will use to analyze the data.

The NC3Rs Experimental Design Assistant can help you structure your experimental plan and identify potential weaknesses before you begin data collection.

Step 4: Collect Data

Conduct the experiment according to your plan. Record all observations, measurements, and relevant contextual information. Maintain detailed records that would allow another researcher to replicate your study. Follow the data management practices outlined in the National Institute of Standards and Technology Research Data Framework to ensure that your data are organized, documented, and preserved.

Step 5: Analyze and Interpret

Analyze the data using the statistical methods specified in your plan. Interpret the results in light of your hypothesis and the existing literature. Consider alternative explanations for your findings and acknowledge the limitations of your study.

Step 6: Communicate Results

Share your findings through appropriate channels. Publish in a peer-reviewed journal, present at a conference, or deposit your data in a repository. Use the reporting guidelines available through the EQUATOR Network to ensure that your report is complete and transparent.

Practical Workflow for the Engineering Design Process

Step 1: Define the Problem

Identify the need or problem that your design will address. Consult with stakeholders, including users, clients, and subject matter experts, to understand the context and requirements. Develop a problem statement that specifies the criteria and constraints for the solution.

Step 2: Research and Gather Information

Research existing solutions, relevant technologies, and applicable standards or regulations. Understand the physical, economic, and social constraints that will shape your design. This research provides the foundation for generating informed design concepts.

Step 3: Generate Multiple Concepts

Brainstorm a wide range of possible solutions. Use techniques such as sketching, modeling, and analogical thinking to explore the solution space. Avoid evaluating ideas too early in the process, as premature judgment can suppress creativity and limit the range of options considered.

Step 4: Select and Develop a Concept

Evaluate the candidate concepts against the criteria and constraints. Consider factors such as performance, cost, manufacturability, reliability, and safety. Select the most promising concept and develop it in detail, specifying the materials, dimensions, and components.

Step 5: Build a Prototype

Construct a prototype of your design. The prototype can be a physical model, a computer simulation, or a functional version of the product. The purpose of the prototype is to test whether the design meets the requirements and to identify areas for improvement.

Step 6: Test and Evaluate

Test the prototype under realistic conditions. Measure its performance against the criteria, observe its behavior, and gather feedback from users. Document the results and compare them with the requirements.

Step 7: Iterate and Refine

Based on the test results, modify the design to address any deficiencies. Repeat the build, test, and refine cycle until the design meets the requirements or until you determine that a different approach is needed. Each iteration should produce measurable improvement in the design.

Observations and Measurements

What to Measure in Scientific Investigations

In scientific investigations, the measurements you collect depend on the hypothesis and the experimental design. Common types of measurements include:

• Quantitative measurements of physical or biological variables, such as concentration, temperature, or growth rate.
• Qualitative observations of behavior, appearance, or other characteristics.
• Outcome measures that capture the effect of the intervention or exposure.

The choice of measurements should be justified by the research question and specified in the experimental plan. Researchers should also measure potential confounding variables so that they can account for them in the analysis.

What to Measure in Engineering Design

In engineering design, measurements focus on the performance of the prototype against the design requirements. Common types of measurements include:

• Functional performance, such as speed, strength, capacity, or accuracy.
• Reliability, such as the number of cycles before failure or the probability of malfunction.
• User satisfaction, such as comfort, ease of use, or aesthetic appeal.
• Cost and resource use, such as material consumption, energy use, or manufacturing time.

The specific measurements depend on the nature of the design and the requirements that were defined at the start of the project. Designers should establish measurable criteria before building the prototype so that testing can be objective and repeatable.

Records and Documentation

Documentation Practices for Scientific Research

Accurate documentation is essential for scientific research. Researchers should maintain:

• Laboratory notebooks or electronic records that capture experimental procedures, observations, and raw data.
• Version-controlled protocols that document any changes to the experimental plan.
• Data management plans that specify how data will be organized, stored, and shared.
• Metadata that describes the context, methods, and quality of the data.

The National Institute of Standards and Technology Research Data Framework provides guidance on the infrastructure and practices needed to manage research data throughout its lifecycle. Following such practices supports reproducibility and enables other researchers to verify or extend the findings.

Documentation Practices for Engineering Design

Engineering design projects also require careful documentation. Designers should maintain:

• Design briefs that capture the problem statement, criteria, and constraints.
• Concept sketches and decision logs that document the evolution of the design.
• Test reports that record the results of prototype testing and evaluation.
• Final specifications that describe the design in sufficient detail for manufacturing or implementation.

Documentation serves multiple purposes in engineering design. It supports communication among team members, provides a record of decisions for future reference, and enables others to understand and build upon the design.

Common Failure Patterns

Failure Patterns in Scientific Investigations

Several common failure patterns can undermine scientific investigations:

• Inadequate sample size, which reduces the power to detect a real effect and increases the risk of false negative results.
• Poorly controlled confounding variables, which can produce spurious associations or mask real effects.
• Biased allocation or measurement, which can systematically distort the results.
• Overinterpretation of results, which can lead to conclusions that are not supported by the evidence.
• Failure to document methods and data, which prevents replication and verification.

Researchers can mitigate these risks by using structured experimental design tools, following reporting guidelines, and maintaining rigorous documentation practices.

Failure Patterns in Engineering Design

Engineering design projects also face common failure patterns:

• Incomplete problem definition, which leads to solutions that do not address the real need.
• Premature commitment to a single concept, which limits the exploration of better alternatives.
• Inadequate testing, which allows design flaws to go undetected until after implementation.
• Ignoring constraints, which can result in solutions that are too expensive, too complex, or impossible to manufacture.
• Poor communication among team members, which can lead to misunderstandings and rework.

The use of teams in the engineering design process can help address some of these failure patterns. A paper on the value of teams in the engineering design process for solving clinical problems addresses whether teams are necessary for effective design. Teams bring diverse perspectives, distribute workload, and provide checks on individual biases, but they also require effective coordination and communication to be productive.

Limitations and Tradeoffs

Limitations of the Scientific Method

The scientific method has inherent limitations that researchers must acknowledge. Experiments are conducted under controlled conditions that may not reflect real-world complexity. Results may not generalize beyond the specific population, setting, or conditions studied. The requirement for controlled experimentation can make some questions difficult or impossible to address through the scientific method.

The quality of the evidence produced by the scientific method depends on the quality of the individual studies. Systematic reviews and meta-analyses can synthesize evidence across multiple studies, but their conclusions are limited by the heterogeneity and quality of the underlying research. The systematic review of behavioral and psychological treatments for chronic insomnia disorder, for example, identified 124 studies that met the inclusion criteria and 89 studies that provided data suitable for statistical analyses. The review used the Grading of Recommendations Assessment, Development, and Evaluation process to evaluate the evidence for making specific treatment recommendations, reflecting the need to assess both the quality of the evidence and the balance of benefits and harms.

Limitations of the Engineering Design Process

The engineering design process also has limitations. Design solutions are context-specific and may not transfer to different settings or requirements. The process can be time-consuming and resource-intensive, particularly when multiple iterations are required. Design decisions involve tradeoffs, and there may be no solution that fully satisfies all criteria and constraints.

The engineering design process also depends on the quality of the problem definition and the accuracy of the requirements. If the requirements are wrong or incomplete, the resulting solution will not meet the actual need, no matter how well the design process is executed. Designers must therefore invest sufficient effort in understanding the problem and validating the requirements with stakeholders.

Safety and Regulatory Context

Safety Considerations in Scientific Research

Scientific research involving human participants, animals, or hazardous materials is subject to ethical and regulatory oversight. Researchers must obtain appropriate approvals before beginning their work, follow established safety protocols, and monitor for adverse events. The specific requirements depend on the jurisdiction and the nature of the research.

Researchers should also consider the broader implications of their work. Studies that produce findings with potential clinical or policy applications should be reported transparently, with appropriate attention to limitations and uncertainties. The EQUATOR Network provides reporting guidelines that help authors describe their studies completely and transparently, supporting the responsible communication of research findings.

Safety Considerations in Engineering Design

Engineering design projects must consider safety throughout the design process. Designers should identify potential hazards, assess the risks associated with their designs, and incorporate safety features to mitigate those risks. The specific safety requirements depend on the application and the applicable regulations and standards.

Designers should also consider the safety of the manufacturing process, the use of the product, and the disposal or recycling of the product at the end of its life. A review of recycled architecture and non-standard material art discusses the use of alternative materials in building design, highlighting the need to balance creativity with the scientific logic of construction. Safety considerations must be integrated into the design from the beginning, not added as an afterthought.

Professional Escalation Criteria

When to Escalate in Scientific Research

Researchers should escalate concerns to supervisors, institutional review boards, or other appropriate authorities in the following situations:

• When the research involves human participants or animals and the required approvals have not been obtained.
• When unexpected adverse events occur during the course of the research.
• When the data reveal patterns that suggest a serious safety or ethical concern.
• When the researcher identifies a conflict of interest that could compromise the integrity of the research.

Early escalation is preferable to delayed escalation. Researchers who are uncertain whether a situation warrants escalation should seek advice from a supervisor or a designated research integrity officer.

When to Escalate in Engineering Design

Designers should escalate concerns to project managers, clients, or regulatory authorities in the following situations:

• When the design requirements cannot be met within the specified constraints.
• When testing reveals a safety hazard that cannot be adequately mitigated.
• When the design is found to violate applicable regulations or standards.
• When the project scope or requirements change in ways that affect the feasibility of the design.

Escalation in engineering design is a normal part of project management. Designers should communicate concerns clearly and promptly, providing the information needed for decision makers to take appropriate action.

Frequently Asked Questions

How does scientific inquiry differ from the scientific method?

Scientific inquiry is the broader process of asking questions about the natural world, gathering evidence, and constructing explanations. The scientific method is a specific, structured approach to testing hypotheses through controlled experimentation. Scientific inquiry encompasses the scientific method but also includes other forms of investigation, such as observational studies, descriptive research, and exploratory analysis. A study of elementary students scientific inquiry processes found that effective students organized their inquiry into iterative cycles of evidence collection, demonstrating a dedicated approach before committing to a final response. This finding illustrates that scientific inquiry involves more than following a fixed sequence of steps, it requires strategic coordination and iterative refinement.

Can the scientific method and engineering design process be used together?

Yes, the two approaches can be used together in a complementary manner. Scientific investigation can provide the knowledge needed to inform design decisions, and engineering design can produce solutions that generate new questions for scientific investigation. A research and development project may move back and forth between the two approaches, using scientific methods to understand underlying principles and engineering design to develop practical applications. The key is to recognize which approach is appropriate for the current stage of the work and to use each approach for its intended purpose.

Which approach is better for solving practical problems?

The engineering design process is generally more appropriate for solving practical problems that require a functional solution. The scientific method is designed to produce knowledge, not artifacts. If the goal is to create a product, system, or process that meets specific requirements, the engineering design process provides the structure needed to define the problem, generate options, build prototypes, and test performance. If the goal is to understand why something happens or to test a causal relationship, the scientific method is the appropriate choice.

How do I know which approach to use for my project?

Consider the primary goal of your project. If you want to answer a question about how the natural world works, use the scientific method. If you want to create a solution to a practical problem, use the engineering design process. If your project involves both understanding and creating, plan to use both approaches at different stages. Define your criteria for success at the outset, and choose the approach that will produce the type of outcome you need.

What are the main similarities between the two approaches?

Both approaches begin with a clear definition of the problem or question, require systematic planning and careful documentation, involve testing ideas against evidence, and are iterative in practice. Both depend on the quality of the underlying reasoning and the rigor of the methods used. Both approaches also benefit from collaboration and diverse perspectives, whether through research teams or design teams.

What are the main differences between the two approaches?

The main difference lies in the goal. The scientific method aims to produce knowledge by testing hypotheses, while the engineering design process aims to produce solutions by designing and testing artifacts. This difference affects the starting point, the core activities, the outcome, and the criteria for success. The scientific method is hypothesis-driven, while the engineering design process is design-driven.

How important is iteration in each approach?

Iteration is important in both approaches, but it takes different forms. In the scientific method, iteration involves refining the hypothesis or the experimental design in response to results. In the engineering design process, iteration is built into the design cycle, with each prototype tested and refined until the design meets the requirements. The engineering design process explicitly plans for multiple cycles of design, build, test, and refine.

What role do teams play in the engineering design process?

Teams can play a valuable role in the engineering design process by bringing diverse perspectives, distributing workload, and providing checks on individual biases. A paper on the value of teams in the engineering design process for solving clinical problems addresses whether teams are necessary for effective design. Teams require effective coordination and communication to be productive, and the benefits of teamwork depend on the nature of the problem and the skills of the team members.

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References and Further Reading

• Research Data Framework. National Institute of Standards and Technology.
• EQUATOR Network. EQUATOR Network.
• Experimental Design Assistant. NC3Rs.
• NCBI Literature Resources. National Center for Biotechnology Information.
• PubMed. National Library of Medicine.
• Strength Training versus Stretching for Improving Range of Motion: A Systematic Review and Meta-Analysis.. Healthcare (Basel, Switzerland), 2021.
• Behavioral and psychological treatments for chronic insomnia disorder in adults: an American Academy of Sleep Medicine systematic review, meta-analysis, and GRADE assessment.. Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine, 2021.
• Interplay of Metabolome and Gut Microbiome in Individuals With Major Depressive Disorder vs Control Individuals.. JAMA psychiatry, 2023.
• Proceedings of the 3rd IPLeiria's International Health Congress : Leiria, Portugal. 6-7 May 2016.. BMC health services research, 2016.
• 25th Annual Computational Neuroscience Meeting: CNS-2016.. BMC neuroscience, 2016.
• Gene Therapy vs Cochlear Implantation in Restoring Hearing Function and Speech Perception for Individuals With Congenital Deafness.. JAMA neurology, 2025.
• Intercostal or Paravertebral Block vs Thoracic Epidural in Lung Surgery: A Randomized Noninferiority Trial.. JAMA surgery, 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.
• From Abstract to Domain-Specific: Development and Validation of Matrix Reasoning Tasks for Students in Biology.. 2026.
• Impact of guided inquiry-based laboratory experiments on secondary school students' attitudes toward biology in Ethiopia: A quasi-experimental study.. 2025.
• Authoritarian attitudes and the perceived scientific legitimacy of anthroposophic medicine: A survey of attitudes on complementary and alternative medicine in Austria.. 2026.
• Macro- and Micro-Level Behavioral Patterns in Simulation-Based Scientific Inquiry: Linking Processes to Performance Among Elementary Students.. 2026.
• The architecture of forensic knowledge: Ontology, transformation, and capture.. 2026.
• Mind over matter? The cognitive styles of scientific scepticism and paranormal belief.. 2026.
• Autophagy Is Suppressed in Peripheral Blood Mononuclear Cells During Chronic Obstructive Pulmonary Disease. 2026.
• Is Working in Teams Necessary to use the Engineering Design Process to Solve Clinical Problems. 2017.
• An Engineering Approach to the Scientific Method. 2017.
• A new human-based metahurestic optimization method based on mimicking cooking training. Scientific Reports, 2022.
• Non-Standard Material Art as an Alternative Development of Recycled Architecture. Journal of Sustainable Architecture and Civil Engineering, 2024.
• Designing a Biomaterial Approach to Control the Adaptive Response to a Skin Injury. Materials, 2022.
• Modeling and optimal design of optical remote sensing payloads. SPIE Optical Engineering + Applications, 2007.
• Integrating Engineering Design Process in STEM Education: A Project Case Study of Design, Creation, and Programming a Crane’s Control Circuit Unit. European Journal of Engineering and Technology Research, 2024.
• Architecture: Integration of Art and Engineering. Buildings, 2022.
• A Systematic Open Source Modeling Method for Energy and Power Systems: Solar PV Application. IEEE Access, 2025.
• 20th International TRIZ Future Conference on Automated Invention for Smart Industries, TFC 2020. IFIP Advances in Information and Communication Technology, 2020.
• 3rd International Scientific Conference on Brain-Computer Interfaces, BCI 2018. Advances in Intelligent Systems and Computing, 2018.

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