Advice for young scientists — and curious people in general

The Nobel Prize-winning biologist Peter Medawar published "Advice to a Young Scientist" in 1979. Here are some of Medawar’s key insights from the book.

Editor's note: This article, which is a selection of quotes from "Advice to a Young Scientists" coupled with commentary from Farnam Street staff, originally ran in May 2021 on the Farnam Street blog. It is reprinted here in its entirety with the gracious permission of Farnam Street.

The Nobel Prize-winning biologist Peter Medawar (1915–1987) is best known for work that made the first organ transplants and skin grafts possible. Medawar was also a lively, witty writer who penned numerous books on science and philosophy.

In 1979, he published Advice to a Young Scientist, a book brimming with both practical advice and philosophical guidance for anyone “engaged in exploratory activities.” Here, we summarize some of Medawar’s key insights from the book.

Application, diligence, a sense of purpose

“There is no certain way of telling in advance if the daydreams of a life dedicated to the pursuit of truth will carry a novice through the frustration of seeing experiments fail and of making the dismaying discovery that some of one’s favourite ideas are groundless.”

If you want to make progress in any area, you need to be willing to give up your best ideas from time to time. 

A black and white profile shot of the Nobel Prize-winning biologist Peter Medawar
The Nobel Prize-winning biologist Peter Medawar (1915–1987) is best known for work that made the first organ transplants and skin grafts possible.
By Digitised for CODEBREAKERS, MAKERS OF MODERN GENETICS

Science proceeds because researchers do all they can to disprove their hypotheses rather than prove them right. Medawar notes that he twice spent two whole years trying to corroborate groundless hypotheses. The key to being a good scientist is the capacity to take no for an answer— when necessary. Additionally:

“…one does not need to be terrifically brainy to be a good scientist…there is nothing in experimental science that calls for great feats of ratiocination or a preternatural gift for deductive reasoning. Common sense one cannot do without, and one would be the better for owning some of those old-fashioned virtues which have fallen into disrepute. I mean application, diligence, a sense of purpose, the power to concentrate, to persevere and not be cast down by adversity—by finding out after long and weary inquiry, for example, that a dearly loved hypothesis is in large measure mistaken.”

The truth is, any measure of risk-taking comes with the possibility of failure. Learning from failure to continue exploring the unknown is a broadly useful mindset.

How to make important discoveries

“It can be said with marked confidence that any scientist of any age who wants to make important discoveries must study important problems. Dull or piffling problems yield dull or piffling answers.”

A common piece of advice for people early on in their careers is to pursue what they find most interesting. Medawar disagrees, explaining that “almost any problem is interesting if it is studied in sufficient depth.” He advises scientists to look for important problems, meaning ones with answers that matter to humankind.

When choosing an area of research, Medawar cautions against mistaking a fashion (“some new histochemical procedure or technical gimmick”) for a movement (“such as molecular genetics or cellular immunology”). Movements lead somewhere; fashions generally don’t.

Getting started

Whenever we begin some new endeavor, it can be tempting to think we need to know everything there is to know about it before we even begin. Often, this becomes a form of procrastination. Only once we try something and our plans make contact with reality can we know what we need to know. Medawar believes it’s unnecessary for scientists to spend an enormous amount of time learning techniques and supporting disciplines before beginning research:

“As there is no knowing in advance where a research enterprise may lead and what kind of skills it will require as it unfolds, this process of ‘equipping oneself’ has no predeterminable limits and is bad psychological policy….The great incentive to learning a new skill or supporting discipline is needing to use it.”

The best way to learn what we need to know is by getting started, then picking up new knowledge as it proves itself necessary. When there’s an urgent need, we learn faster and avoid unnecessary learning. The same can be true for too much reading:

“Too much book learning may crab and confine the imagination, and endless poring over the research of others is sometimes psychologically a research substitute, much as reading romantic fiction may be a substitute for real-life romance….The beginner must read, but intently and choosily and not too much.”

We don’t talk about this much at Farnam Street, but it is entirely possible to read too much. Reading becomes counterproductive when it serves as a substitute for doing the real thing, if that’s what someone is reading for. Medawar explains that it is “psychologically most important to get results, even if they are not original.” It’s important to build confidence by doing something concrete and seeing a visible manifestation of our labors. For Medawar, the best scientists begin with the understanding that they can never know anything and, besides, learning needs to be a lifelong process.

The secrets to effective collaboration

“Scientific collaboration is not at all like cooks elbowing each other from the pot of broth; nor is it like artists working on the same canvas, or engineers working out how to start a tunnel simultaneously from both sides of a mountain in such a way that the contractors do not miss each other in the middle and emerge independently at opposite ends.”

Instead, scientific collaboration is about researchers creating the right environment to develop and expand upon each other’s ideas. A good collaboration is greater than the sum of its parts and results in work that isn’t attributable to a single person.

For scientists who find their collaborators infuriating from time to time, Medawar advises being self-aware. We all have faults, and we too are probably almost intolerable to work with sometimes.

When collaboration becomes contentious, Medawar maintains that we should give away our best ideas.

Scientists sometimes face conflict over the matter of credit. If several researchers are working on the same problem, whichever one finds the solution (or a solution) first gets the credit, no matter how close the others were. This is a problem most creative fields don’t face: “The twenty years Wagner spent on composing the first three operas of The Ring were not clouded by the fear that someone else might nip ahead of him with Götterdämmerung.” Once a scientific idea becomes established, it becomes public property. So the only chance of ownership a researcher has comes by being the first.

However, Medawar advocates for being open about ideas and doing away with secrecy because “anyone who shuts his door keeps out more than he lets out.” He goes on to write, “The agreed house rule of the little group of close colleagues I have always worked with has always been ‘Tell everyone everything you know,’ and I don’t know anyone who came to any harm by falling in with it.

How to handle moral dilemmas

A scientist will normally have contractual obligations to his employer and has always a special and unconditionally binding obligation to the truth.

Medawar writes that many scientists, at some point in their career, find themselves grappling with the conflict between a contractual obligation and their own conscience. However, the “time to grapple is before a moral dilemma arises.” If we think an enterprise might lead somewhere damaging, we shouldn’t start on it in the first place.

We should know our values and aim to do work in accordance with them.

The first rule is never to fool yourself

“I cannot give any scientist of any age better advice than this: the intensity of the conviction that a hypothesis is true has no bearing of whether it is true or not.”

Richard Feynman famously said, “The first principle is that you must not fool yourself—and you are the easiest person to fool.” All scientists make mistakes sometimes. Medawar advises, when this happens, to issue a swift correction. To do so is far more respectable and beneficial for the field than trying to cover it up. Echoing the previous advice to always be willing to take no for an answer, Medawar warns about falling in love with a hypothesis and believing it is true without evidence.

“A scientist who habitually deceives himself is well on the way toward deceiving others.”

The best creative environment

“To be creative, scientists need libraries and laboratories and the company of other scientists; certainly a quiet and untroubled life is a help. A scientist’s work is in no way deepened or made more cogent by privation, anxiety, distress, or emotional harassment. To be sure, the private lives of scientists may be strangely and comically mixed up, but not in ways that have any special bearing on the nature and quality of their work.”

Creativity rises from tranquility, not from disarray. Creativity is supported by a safe environment, one in which you can share and question openly and be heard with compassion and a desire to understand.

A final piece of advice

“A scientist who wishes to keep his friends and not add to the number of his enemies must not be forever scoffing and criticizing and so earn a reputation for habitual disbelief; but he owes it to his profession not to acquiesce in or appear to condone folly, superstition, or demonstrably unsound belief. The recognition and castigation of folly will not win him friends, but it may gain him some respect.”

Related content

US, WA, Seattle
Amazon internships are full-time (40 hours/week) for 12 consecutive weeks with start dates in May - July 2023. Our internship program provides hands-on learning and building experiences for students who are interested in a career in hardware engineering. This role will be based in Seattle, and candidates must be willing to work in-person.Corporate Projects (CPT) is a team that sits within the broader Corporate Development organization at Amazon. We seek to bring net-new, strategic projects to life by working together with customers and evolving projects from ZERO-to-ONE. To do so, we deploy our resources towards proofs-of-concept (POCs) and pilot programs and develop them from high-level ideas (the ZERO) to tangible short-term results that provide validating signal and a path to scale (the ONE). We work with our customers to develop and create net-new opportunities by relentlessly scouring all of Amazon and finding new and innovative ways to strengthen and/or accelerate the Amazon Flywheel.CPT seeks an Applied Science intern to work with a diverse, cross-functional team to build new, innovative customer experiences. Within CPT, you will apply both traditional and novel scientific approaches to solve and scale problems and solutions. We are a team where science meets application. A successful candidate will be a self-starter comfortable with ambiguity, strong attention to detail, and the ability to work in a fast-paced, ever-changing environment. As an Applied Science Intern, you will own the design and development of end-to-end systems. You’ll have the opportunity to create technical roadmaps, and drive production level projects that will support Amazon Science. You will work closely with Amazon scientists, and other science interns to develop solutions and deploy them into production. The ideal scientist must have the ability to work with diverse groups of people and cross-functional teams to solve complex business problems.
US, WA, Seattle
Note that this posting is for a handful of teams within Amazon Robotics. Teams include: Robotics, Computer Vision, Machine Learning, Optimization, and more.Are you excited about building high-performance robotic systems that can perceive and learn to help deliver for customers? The Amazon Robotics team is creating new science products and technologies that make this possible, at Amazon scale. We work at the intersection of computer vision, machine learning, robotic manipulation, navigation, and human-robot interaction.Amazon Robotics is seeking broad, curious applied scientists and engineering interns to join our diverse, full-stack team. In addition to designing, building, and delivering end-to-end robotic systems, our team is responsible for core infrastructure and tools that serve as the backbone of our robotic applications, enabling roboticists, applied scientists, software and hardware engineers to collaborate and deploy systems in the lab and in the field. We will give you the tools and support you need to invent with us in ways that are rewarding, fulfilling and fun. Come join us!A day in the lifeAs an intern you will develop a new algorithm to solve one of the challenging computer vision and manipulation problems in Amazon's robotic warehouses. Your project will fit your academic research experience and interests. You will code and test out your solutions in increasingly realistic scenarios and iterate on the idea with your mentor to find the best solution to the problem.
US, WA, Bellevue
The Global Supply Chain-ACES organization aims to raise the bar on Amazon’s customer experience by delivering holistic solutions for Global Customer Fulfillment that facilitate the effective and efficient movement of product through our supply chain. We develop strategies, processes, material handling and technology solutions, reporting and other mechanisms, which are simple, technology enabled, globally scalable, and locally relevant. We achieve this through cross-functional partnerships, listening to the needs of our customers and prioritizing initiatives to deliver maximum impact across the value chain. Within the organization, our Quality team balances tactical operation with operations partners with global engagement on programs to deliver improved inventory accuracy in our network. The organization is looking for an experienced Principal Data Scientist to partner with senior leadership to develop long term strategic solutions. As a Principal Scientist, they will lead critical initiatives for Global Supply Chain, leveraging complex data analysis and visualization to:a. Collaborate with business teams to define data requirements and processes;b. Automate data pipelines;c. Design, develop, and maintain scalable (automated) reports and dashboards that track progress towards plans;d. Define, track and report program success metrics.e. Serve as a technical science lead on our most demanding, cross-functional projects.
US, WA, Seattle
Are you excited about building high-performance robotic systems that can perceive, learn, and act intelligently alongside humans? The Robotics AI team is creating new science products and technologies that make this possible, at Amazon scale. We work at the intersection of computer vision, machine learning, robotic manipulation, navigation, and human-robot interaction.The Amazon Robotics team is seeking broad, curious applied scientists and engineering interns to join our diverse, full-stack team. In addition to designing, building, and delivering end-to-end robotic systems, our team is responsible for core infrastructure and tools that serve as the backbone of our robotic applications, enabling roboticists, applied scientists, software and hardware engineers to collaborate and deploy systems in the lab and in the field. Come join us!
US, WA, Bellevue
Employer: Amazon.com Services LLCPosition: Research Scientist IILocation: Bellevue, WA Multiple Positions Available1. Research, build and implement highly effective and innovative methods in Statistical Modeling, Machine Learning, and other quantitative techniques such as operational research and optimization to deliver algorithms that solve real business problems.2. Take initiative to scope and plan research projects based on roadmap of business owners and enable data-driven solutions. Participate in shaping roadmap for the research team.3. Ensure data quality throughout all stages of acquisition and processing of the data, including such areas as data sourcing/collection, ground truth generation, data analysis, experiment, evaluation and visualization etc.4. Navigate a variety of data sources, understand the business reality behind large-scale data and develop meaningful science solutions.5. Partner closely with product or/and program owners, as well as scientists and engineers in cross-functional teams with a clear path to business impact and deliver on demanding projects.6. Present proposals and results in a clear manner backed by data and coupled with conclusions to business customers and leadership team with various levels of technical knowledge, educating them about underlying systems, as well as sharing insights.7. Perform experiments to validate the feature additions as requested by domain expert teams.8. Some telecommuting benefits available.The pay range for this position in Bellevue, WA is $136,000-$184,000 (yr); however, base pay offered may vary depending on job-related knowledge, skills, and experience. A sign-on bonus and restricted stock units may be provided as part of the compensation package, in addition to a full range of medical, financial, and/or other benefits, dependent on the position offered. This information is provided by the Washington Equal Pay Act. Base pay information is based on market location. Applicants should apply via Amazon's internal or external careers site.#0000
US, VA, Arlington
The Central Science Team within Amazon’s People Experience and Technology org (PXTCS) uses economics, behavioral science, statistics, and machine learning to proactively identify mechanisms and process improvements which simultaneously improve Amazon and the lives, well-being, and the value of work to Amazonians. We are an interdisciplinary team, which combines the talents of science and engineering to develop and deliver solutions that measurably achieve this goal. As Director for PXT Central Science Technology, you will be responsible for leading multiple teams through rapidly evolving complex demands and define, develop, deliver and execute on our science roadmap and vision. You will provide thought leadership to scientists and engineers to invent and implement scalable machine learning recommendations and data driven algorithms supporting flexible UI frameworks. You will manage and be responsible for delivering some of our most strategic technical initiatives. You will design, develop and operate new, highly scalable software systems that support Amazon’s efforts to be Earth’s Best Employer and have a significant impact on Amazon’s commitment to our employees and communities where we both serve and employ 1.3 million Amazonians. As Director of Applied Science, you will be part of the larger technical leadership community at Amazon. This community forms the backbone of the company, plays a critical role in the broad business planning, works closely with senior executives to develop business targets and resource requirements, influences our long-term technical and business strategy, helps hire and develop engineering leaders and developers, and ultimately enables us to deliver engineering innovations.This role is posted for Arlington, VA, but we are flexible on location at many of our offices in the US and Canada.
US, VA, Arlington
Employer: Amazon.com Services LLCPosition: Data Scientist IILocation: Arlington, VAMultiple Positions Available1. Manage and execute entire projects or components of large projects from start to finish including data gathering and manipulation, synthesis and modeling, problem solving, and communication of insights and recommendations.2. Oversee the development and implementation of data integration and analytic strategies to support population health initiatives.3. Leverage big data to explore and introduce areas of analytics and technologies.4. Analyze data to identify opportunities to impact populations.5. Perform advanced integrated comprehensive reporting, consultative, and analytical expertise to provide healthcare cost and utilization data and translate findings into actionable information for internal and external stakeholders.6. Oversee the collection of data, ensuring timelines are met, data is accurate and within established format.7. Act as a data and technical resource and escalation point for data issues, ensuring they are brought to resolution.8. Serve as the subject matter expert on health care benefits data modeling, system architecture, data governance, and business intelligence tools. #0000
US, TX, Dallas
Employer: Amazon.com Services LLCPosition: Data Scientist II (multiple positions available)Location: Dallas, TX Multiple Positions Available:1. Assist customers to deliver Machine Learning (ML) and Deep Learning (DL) projects from beginning to end, by aggregating data, exploring data, building and validating predictive models, and deploying completed models to deliver business impact to the organization;2. Apply understanding of the customer’s business need and guide them to a solution using AWS AI Services, AWS AI Platforms, AWS AI Frameworks, and AWS AI EC2 Instances;3. Use Deep Learning frameworks like MXNet, PyTorch, Caffe 2, Tensorflow, Theano, CNTK, and Keras to help our customers build DL models;4. Research, design, implement and evaluate novel computer vision algorithms and ML/DL algorithms;5. Work with data architects and engineers to analyze, extract, normalize, and label relevant data;6. Work with DevOps engineers to help customers operationalize models after they are built;7. Assist customers with identifying model drift and retraining models;8. Research and implement novel ML and DL approaches, including using FPGA;9. Develop computer vision and machine learning methods and algorithms to address real-world customer use-cases; and10. Design and run experiments, research new algorithms, and work closely with engineers to put algorithms and models into practice to help solve customers' most challenging problems.11. Approximately 15% domestic and international travel required.12. Telecommuting benefits are available.#0000
US, WA, Seattle
MULTIPLE POSITIONS AVAILABLECompany: AMAZON.COM SERVICES LLCPosition Title: Manager III, Data ScienceLocation: Bellevue, WashingtonPosition Responsibilities:Manage a team of data scientists working to build large-scale, technical solutions to increase effectiveness of Amazon Fulfillment systems. Define key business goals and map them to the success of technical solutions. Aggregate, analyze and model data from multiple sources to inform business decisions. Manage and quantify improvement in the customer experience resulting from research outcomes. Develop and manage a long-term research vision and portfolio of research initiatives, with algorithms and models that to be integrated in production systems. Hire and mentor junior scientists.Amazon.com is an Equal Opportunity-Affirmative Action Employer – Minority / Female / Disability / Veteran / Gender Identity / Sexual Orientation #0000
US, VA, Arlington
MULTIPLE POSITIONS AVAILABLECompany: AMAZON.COM SERVICES LLCPosition Title: Data Scientist IILocation: Arlington, VirginiaPosition Responsibilities:Design and implement scalable and reliable approaches to support or automate decision making throughout the business. Apply a range of data science techniques and tools combined with subject matter expertise to solve difficult business problems and cases in which the solution approach is unclear. Acquire data by building the necessary SQL / ETL queries. Import processes through various company specific interfaces for accessing Oracle, RedShift, and Spark storage systems. Build relationships with stakeholders and counterparts. Analyze data for trends and input validity by inspecting univariate distributions, exploring bivariate relationships, constructing appropriate transformations, and tracking down the source and meaning of anomalies. Build models using statistical modeling, mathematical modeling, econometric modeling, network modeling, social network modeling, natural language processing, machine learning algorithms, genetic algorithms, and neural networks. Validate models against alternative approaches, expected and observed outcome, and other business defined key performance indicators. Implement models that comply with evaluations of the computational demands, accuracy, and reliability of the relevant ETL processes at various stages of production.Amazon.com is an Equal Opportunity-Affirmative Action Employer – Minority / Female / Disability / Veteran / Gender Identity / Sexual Orientation #0000