experiment proper meaning

How is the noun experiment pronounced?

British english, u.s. english, where does the noun experiment come from.

Earliest known use

Middle English

The earliest known use of the noun experiment is in the Middle English period (1150—1500).

OED's earliest evidence for experiment is from before 1382, in Bible (Wycliffite, early version) .

experiment is of multiple origins. Partly a borrowing from French. Partly a borrowing from Latin.

Etymons: French experiment ; Latin experīmentum .

Nearby entries

• experiencing, adj. 1697–
• experiency, n. 1556
• experient, adj. & n. ?1440–
• experiential, adj. 1658–
• experientialism, n. 1865–
• experientialist, n. 1866–
• experientially, adv. 1647–
• experiential philosopher, n. 1866–
• experiential philosophy, n. 1848–
• experiently, adv. 1473–1849
• experiment, n. a1382–
• experiment, v. 1481–
• experimental, adj. & n. c1449–
• experimental archaeology | experimental archeology, n. 1961–
• experimental farm, n. 1739–
• experimentalism, n. 1808–
• experimentalist, n. & adj. 1677–
• experimentalize, v. 1751–
• experimentalizer, n. 1831–
• experimentalizing, n. 1809–1929
• experimentally, adv. a1460–

Thank you for visiting Oxford English Dictionary

To continue reading, please sign in below or purchase a subscription. After purchasing, please sign in below to access the content.

Meaning & use

Pronunciation, compounds & derived words, entry history for experiment, n..

experiment, n. was revised in March 2024.

experiment, n. was last modified in June 2024.

oed.com is a living text, updated every three months. Modifications may include:

• further revisions to definitions, pronunciation, etymology, headwords, variant spellings, quotations, and dates;
• new senses, phrases, and quotations.

Revisions and additions of this kind were last incorporated into experiment, n. in June 2024.

Earlier versions of this entry were published in:

OED First Edition (1894)

• Find out more

OED Second Edition (1989)

• View experiment, n. in OED Second Edition

Please submit your feedback for experiment, n.

Please include your email address if you are happy to be contacted about your feedback. OUP will not use this email address for any other purpose.

Citation details

Factsheet for experiment, n., browse entry.

• Britannica Homepage
• Ask the Editor
• Word of the Day
• Core Vocabulary
• Most Popular
• Browse the Dictionary
• My Saved Words
• experiment (noun)
• experiment (verb)
• Students will carry out simple laboratory experiments .
• perform/conduct/do/run an experiment
• a failed experiment
• They did some experiments with magnets.
• a series of experiments on rats [=done to rats]
• These theories have not yet been confirmed by experiment .
• I'd like to paint the room a different color, just as an experiment . [=to see if it looks good or not]
• an experiment in living more frugally
• the city's experiment with a longer school year
• They experimented with magnets.
• researchers experimenting on rats
• an artist who's always experimenting [=trying new things]
• He's been experimenting with various materials.
• She experimented with different kinds of weaving.
• The school is experimenting with a longer school year.
• teenagers experimenting with drugs [=using illegal drugs to find out if they like them]

— experimentation

• scientific experimentation with/on rats
• artistic experimentation

— experimenter

• About Us & Legal Info
• Partner Program
• Privacy Notice
• Terms of Use
• Pronunciation Symbols

What Is an Experiment? Definition and Design

The Basics of an Experiment

• Chemical Laws
• Periodic Table
• Projects & Experiments
• Scientific Method
• Biochemistry
• Physical Chemistry
• Medical Chemistry
• Chemistry In Everyday Life
• Famous Chemists
• Activities for Kids
• Abbreviations & Acronyms
• Weather & Climate
• Ph.D., Biomedical Sciences, University of Tennessee at Knoxville
• B.A., Physics and Mathematics, Hastings College

Science is concerned with experiments and experimentation, but do you know what exactly an experiment is? Here's a look at what an experiment is... and isn't!

Key Takeaways: Experiments

• An experiment is a procedure designed to test a hypothesis as part of the scientific method.
• The two key variables in any experiment are the independent and dependent variables. The independent variable is controlled or changed to test its effects on the dependent variable.
• Three key types of experiments are controlled experiments, field experiments, and natural experiments.

What Is an Experiment? The Short Answer

In its simplest form, an experiment is simply the test of a hypothesis . A hypothesis, in turn, is a proposed relationship or explanation of phenomena.

Experiment Basics

The experiment is the foundation of the scientific method , which is a systematic means of exploring the world around you. Although some experiments take place in laboratories, you could perform an experiment anywhere, at any time.

Take a look at the steps of the scientific method:

• Make observations.
• Formulate a hypothesis.
• Design and conduct an experiment to test the hypothesis.
• Evaluate the results of the experiment.
• Accept or reject the hypothesis.
• If necessary, make and test a new hypothesis.

Types of Experiments

• Natural Experiments : A natural experiment also is called a quasi-experiment. A natural experiment involves making a prediction or forming a hypothesis and then gathering data by observing a system. The variables are not controlled in a natural experiment.
• Controlled Experiments : Lab experiments are controlled experiments , although you can perform a controlled experiment outside of a lab setting! In a controlled experiment, you compare an experimental group with a control group. Ideally, these two groups are identical except for one variable , the independent variable .
• Field Experiments : A field experiment may be either a natural experiment or a controlled experiment. It takes place in a real-world setting, rather than under lab conditions. For example, an experiment involving an animal in its natural habitat would be a field experiment.

Variables in an Experiment

Simply put, a variable is anything you can change or control in an experiment. Common examples of variables include temperature, duration of the experiment, composition of a material, amount of light, etc. There are three kinds of variables in an experiment: controlled variables, independent variables and dependent variables .

Controlled variables , sometimes called constant variables are variables that are kept constant or unchanging. For example, if you are doing an experiment measuring the fizz released from different types of soda, you might control the size of the container so that all brands of soda would be in 12-oz cans. If you are performing an experiment on the effect of spraying plants with different chemicals, you would try to maintain the same pressure and maybe the same volume when spraying your plants.

The independent variable is the one factor that you are changing. It is one factor because usually in an experiment you try to change one thing at a time. This makes measurements and interpretation of the data much easier. If you are trying to determine whether heating water allows you to dissolve more sugar in the water then your independent variable is the temperature of the water. This is the variable you are purposely controlling.

The dependent variable is the variable you observe, to see whether it is affected by your independent variable. In the example where you are heating water to see if this affects the amount of sugar you can dissolve , the mass or volume of sugar (whichever you choose to measure) would be your dependent variable.

Examples of Things That Are Not Experiments

• Making a model volcano.
• Making a poster.
• Changing a lot of factors at once, so you can't truly test the effect of the dependent variable.
• Trying something, just to see what happens. On the other hand, making observations or trying something, after making a prediction about what you expect will happen, is a type of experiment.
• Bailey, R.A. (2008). Design of Comparative Experiments . Cambridge: Cambridge University Press. ISBN 9780521683579.
• Beveridge, William I. B., The Art of Scientific Investigation . Heinemann, Melbourne, Australia, 1950.
• di Francia, G. Toraldo (1981). The Investigation of the Physical World . Cambridge University Press. ISBN 0-521-29925-X.
• Hinkelmann, Klaus and Kempthorne, Oscar (2008). Design and Analysis of Experiments, Volume I: Introduction to Experimental Design (Second ed.). Wiley. ISBN 978-0-471-72756-9.
• Shadish, William R.; Cook, Thomas D.; Campbell, Donald T. (2002). Experimental and quasi-experimental designs for generalized causal inference (Nachdr. ed.). Boston: Houghton Mifflin. ISBN 0-395-61556-9.
• 10 Things You Need To Know About Chemistry
• Chemistry 101 - Introduction & Index of Topics
• How to Clean Lab Glassware
• How To Design a Science Fair Experiment
• Understanding Experimental Groups
• What Is a Control Group?
• Examples of Independent and Dependent Variables
• How to Write a Lab Report
• The Difference Between Control Group and Experimental Group
• Scientific Method Lesson Plan
• Pre-Lab Prep for Chemistry Lab
• Difference Between Independent and Dependent Variables
• Which Is Faster: Melting Ice in Water or Air?
• What Is the Difference Between Hard and Soft Science?
• 5 Top Reasons Why Students Fail Chemistry
• What Is a Dependent Variable?

• Arts Culture & Society

Science, Technology & Innovation

Arts, culture & society, teaching & learning, inclusivity & diversity, crisis management, leadership & management, entrepreneurship & employability, globalisation & regionalisation, policy, governance & reform, future & sustainability.

• December 21, 2021

What are Experiments, and why do we need them?

WHAT IS AN EXPERIMENT?

The Merriam-Webster dictionary defines an experiment as “an operation or procedure carried out under controlled conditions in order to discover an unknown effect or law, to test or establish a hypothesis, or to illustrate a known law.”

The main goals of experimentation are twofold. Firstly, it allows us to compare two or more variants of a product, method, policy or theory, in order to determine which performs better. Secondly, it allows us to establish causality – whether doing X (for example, having a larger “Subscribe” button) will result in Y (an increase in subscriptions).

Imagine that a primary school recently introduced tablets to help primary school students expand their vocabulary. Take a look at Figure 1, which shows the vocabulary size of students in this hypothetical situation:

The dotted line represents vocabulary size before the introduction of tablets, and the solid line represents vocabulary size after introducing tablets.

Looking at the larger vocabulary size after the introduction of tablets, one might be tempted to conclude that tablets do indeed improve learning. However, it is not so simple. The larger vocabulary size after the introduction of tablets could have come from a wide variety of sources. For example, perhaps the introduction of tablets coincided with a new lesson in which students were learning easier words. Or, it could simply just be due to the natural improvement in learning that a child experiences with age.

To circumvent the problem of external factors exerting an influence on our question of interest (in this case, whether tablets improve vocabulary size), we can conduct an experiment. For centuries, scientists and academics have used experiments to uncover causal relationships and test theories and hypotheses. In our hypothetical example, we could split our students into two groups – one group will receive tablets, and the other will not. Figure 2 shows some hypothetical data from this experiment.

Now, we have greater confidence that tablets do indeed improve vocabulary size. Even though vocabulary size was increasing in both groups, the group with the tablets have had a much larger increase, suggesting that tablets are indeed advantageous in boosting vocabulary learning.

However, consider a different scenario shown in Figure 3.

Here, there is no meaningful difference between the vocabulary scores of the two groups after tablets were introduced. If we had only relied on observing the scores before and after the introduction of tablets without conducting an experiment, we would have mistakenly concluded that the increase in vocabulary score was due to the tablets, when in fact it could have just been due to random factors or to the natural increase in a child’s vocabulary size throughout the semester.

Herein lies the power of experiments – they allow us to confidently make conclusions about the effectiveness of an intervention.

Although you might not realise this, if you are a user of technology (mobile apps, social media, e-commerce, the internet, and so on), you are almost certainly a participant in multiple experiments.

TECHNOLOGY, BIG DATA AND EXPERIMENTATION

Recent advances in computing power, data storage and technology in general have made it much easier, more cost-effective, and less time- consuming to conduct experiments. Indeed, the number and type of experiments that have been conducted by both tech companies and research institutes has undergone an explosive growth in the last five years. Many companies are constantly running consumer-facing experiments, testing different versions of their apps and features in order to determine which one performs the best – in terms of usability, retaining and gaining users, and, of course, profit. Tech companies such as Google, Amazon, Grab, Meta (Facebook), and Amazon have large in-house experimentation teams and platforms that allow them to quickly and effectively experiment on any product and feature launches and changes in order to make informed, data-driven decisions. Annually, each of these companies conducts more than 10,000 experiments; many of these have hundreds of thousands, if not millions, of users.

Two different versions of the Facebook page design

The new page layout (right) was designed to be less complex and to highlight key information. Facebook ran experiments to determine whether the new layout was successful in achieving this. Source: www.techcrunch.com

In the world of academia and research, many companies such as Amazon MTurk, Prolific and Pavlova were started to offer the ability to conduct online experiments, allowing them to collect data more quickly compared to the traditional method of lab-based experiments. In many cases, experiments that would have taken months of data gathering can now be completed in days or even hours.

Although you might not realise this, if you are a user of technology (mobile apps, social media, e-commerce, the internet, and so on), you are almost certainly a participant in multiple experiments. Open up your favourite app and compare it to someone else’s. Chances are, the interface, ads, or even the features you see will be different.

This is experimentation in action – companies are constantly testing different versions to find out which one performs the best. Does changing the size of the “Subscribe” button increase the number of subscribers of a newsletter? Which email title leads to a higher open rate? Will customers be more likely to make a purchase if we show them how many times an item has been purchased in the last hour? These are some of the questions that can be answered through experiments, rather than relying on ‘gut feel’ or intuition, or the nebulous concept of ‘past experience’.

ETHICS AND CONSENT

The ubiquity of experiments in our everyday lives has raised some important questions about consent and ethics. The majority of users of technology are probably in the dark about what kind of experiments they are part of – or even about the simple fact that they have been a part of multiple experiments. Even though we might not explicitly give consent, the terms and conditions that we (often blindly) accept already grant companies the right to conduct experiments (among many other things).

Thankfully, tech companies conduct experiments generally with the aim of improving user experience, increasing customer satisfaction, and bringing value to their customers. Of course, it is undeniable that the bottom line of most companies is profit – but it is also understood that the customer always comes first and that there would be no profit without customers.

Facebook's emotion experiment

For one week in 2012, Facebook’s data scientists manipulated the News Feeds of 689,003 users, removing either all of the positive posts or all of the negative posts, to see how it affected users’ moods. This raises deep concerns about the ethics of emotional manipulation and guidelines for informed consent of human subjects. Source: iStock

If experiments are inevitable, what can, and should, companies do to prevent harm to their customers in the course of their being conducted?

However, there have been unfortunate instances in which companies crossed ethical boundaries in their experimentation. In 2014, it was revealed that Facebook had run an experiment that breached ethical guidelines for informed consent. This was especially grievous – the experiment brought potential harm to their users because it was designed to change their mood and even induce some emotional distress.

Biomedical and behavioural research involving human participants in academia, healthcare and government is governed by a set of research ethics that is approved by an Institutional Review Board (IRB). One of the core principles of experiments involving human participants is that the risk of harm from the experiment should not be “greater than those ordinarily encountered in daily life”. In the Facebook experiment, a group of users were purposefully shown more emotionally negative content on their Facebook News Feed (“negative group”), while another group were shown more emotionally positive content (“positive group”).

In essence, Facebook’s experiment manipulated the mood of their users. The researchers found that those in the negative group began posting more negative updates, while those in the positive group began posting more positive updates. This effect was large enough to be published in a top-tier scientific journal, PNAS (Proceedings of the National Academy of Sciences of the United States of America), even though Facebook claimed that the effect was not large – after they were questioned on the ethics of the experiment.

If experiments are inevitable, what can, and should, companies do to prevent harm to their customers in the course of their being conducted? Companies could consider allowing users to opt out of experiments, similar to how users are allowed to opt out of data collection. However, this could ultimately have an impact on the end user themselves, since they would then not have an experience that is optimised by experiments and data.

At the very least, data scientists and researchers who design and implement experiments should receive training in ethics. Currently, researchers in academia, healthcare and government organisations receive mandatory training in ethics and compliance, especially if they work with human populations. While it might be a mere first step, it is at least one in the right direction towards the protection of consumer rights.

The Facebook watchdog

Maria Ressa was awarded the 2021 Nobel Peace Prize jointly with Dmitry Muratov for “their efforts to safeguard freedom of expression” amid growing concerns over curbs on free speech worldwide. She is calling for technology companies to face greater regulation globally to curb disinformation on social media. “Without facts, you can't have truth. Without truth, you can't have trust. Without trust, we have no shared reality, no democracy, and it becomes impossible to deal with our world's existential problems: climate, coronavirus, the battle for truth.” Photo: PACIFIC PRESS / Alamy Live News

DR GAVIN NG

Dr Gavin Ng obtained his Ph.D. in Experimental Psychology from the University of Illinois at Urbana-Champaign. There, he conducted controlled experiments to understand the human visual system – the processes and the computations that the brain carries out to perceive objects (e.g., their shape, colour, and form), deploy attention and eye movements, and search for things in the environment. He recently relocated back home to Singapore and is currently a Senior Data Scientist at Grab, where he continues to design, run and analyse experiments, albeit with larger datasets and in a less controlled setting with noisier data.

DECEMBER 2021 | ISSUE 9

Tomorrow's Technology Today

Returns to schooling: Driven by family background or education-related policies?

Ageing, Pain, and Dying

Artful Ageing: The Unstoppable Creative Journey of the Centenarian Artist Lim Tze Peng

Join our mailing list, stay updated on all the latest news and events.

20 Upper Circular Road The Riverwalk, #02-21 Singapore 058416 +65 6672 6160 [email protected]

Let’s connect.

Do you have any questions about our projects or our site in general? Do you have any comments or ideas you would like to share? Please feel free to call us, email us or simply send us a message from here.

Copyright © 2024 The HEAD Foundations. All Rights Reserved.

• Terms Of Use
• Privacy Policy

Leaders and changemakers of today face unique and complex challenges. The HEAD Foundation Digest features insights and opinions from those in the know addressing a wide range of pertinent issues that factor in a society’s development.  Informed opinions can inspire healthy discussions and open up our imagination to new possibilities. Interested in contributing? Write to us at info@headfoundation

Stay updated on our latest announcements on events and publications

• CONTRIBUTORS
• THE HEAD FOUNDATION MAIN WEBSITE

Leaders and changemakers of today face unique and complex challenges. The HEAD Foundation Digest features insights and opinions from those in the know addressing a wide range of pertinent issues that factor in a society’s development.  Informed opinions can inspire healthy discussions and open up our imagination to new possibilities. Interested in contributing? Write to us at info@headfoundation

What Do Psychologists Mean When They Say "Experiment"?

Control groups and control conditions allow for vital comparisons..

Posted August 29, 2021 | Reviewed by Devon Frye

• Control is one of the key features of an experiment.
• This means using control groups or control conditions for comparison.
• The quality of comparison matters—we can't just compare doing something with doing nothing.

What makes an experiment, an experiment?

In the last, first post of this blog, I mentioned that much of research methods is trying to make sure we draw the right conclusions, while also trying very hard not to draw the wrong conclusions. The type of study particularly suited for this is the experiment . Contrary to popular belief, not every study is an experiment—in fact, in psychological research, the term "experiment" is narrowly defined as a study involving both randomisation and control. In this blog post, I want to explain what we mean by control and why it is such an important part of research.

Let’s assume a group of researchers wants to find out how to improve childrens’ working memory in the long term. In fact, we don't need to assume because that's precisely what researchers Henry, Messer, and Nash wanted to find out in their 2013 study . In particular, they want to test whether adaptive "executive-loaded exercises" are effective in training children working memory. "Executive-loaded" means these are exercises that put a cognitive load on the executive function , i.e. the part of the brain that allocates cognitive resources and attention ; adaptive means they adjust to the children's skill and get easier or more difficult as the children progress.

Surely the easiest way for Henry et al. would have been to test the childrens’ working memory to establish a baseline for comparison, then train them with this set of exercises, then test their working memory again, right?

Simple Comparisons Don't Work

Let's assume for a moment that's what they did. In principle, there are three(ish) possible outcomes in such a situation: the second set of tests show that children perform better in the same working memory tests; they perform equally well; or they perform worse. Luckily for the researchers, the tests show an improvement. Would that allow Henry and colleagues to conclude that these exercises helped children to improve their working memory?

Sadly no. As is the case with most of us,1 we continue to learn and improve our skills —and of course, this is particularly true for children. In other words, there is a distinct chance that the children in the study would have improved over time anyway, and the researchers would not be able to say with confidence that any improvement they might have seen is due to their training method.

What the researchers need, therefore, is a way of finding out whether the improvement is (only) due to the passage of time or, at least partially, to them training the children in this method. They need another group of children who don’t get trained in executive-loaded exercises. This helps establish whether any improvement they observe would have happened anyway, or whether it’s a consequence of the intervention (i.e., the training method). If both groups show roughly equivalent 1 improvement, it’s unlikely 2 that our method made any difference; however, if the comparison group does not improve, and ours does, we are slightly more justified in concluding that method X might have some merit.

We Need Control

In psychological research, such a comparison group is also called a control group as it’s essentially controlling for the passage of time and the change of skills, abilities, opinions, and experiences that go with it. We also refer to the two groups as conditions , as in “Group 1 experiences condition X, group 2 experiences condition Y.”

But simply having a control group isn’t enough. It’s also important how that control group is selected, and what the control group experiences. In the study by Henry et al., the intervention consisted of repeated in-person meetings with an experimenter. But it could also have consisted of children coming into the psychology department with their parents and spending some time with the researcher during training. Or perhaps the researcher(s) paid house visits to the children and their families.

In any of these cases, the children in our intervention group did receive some more attention and interaction from their parents and/or the researcher(s) than they usually would have—and more than the control group, if control just means doing nothing! You may have heard this referred to as the Hawthorne effect , after research at the eponymous production plant which found that workers’ productivity in a factory improved regardless of the actual intervention (e.g., more light, less light) and eventually concluded that it was the existence of the intervention and the resulting increase in attention that improved workers’ productivity.

… But Not Just Any Kind of Control

Whether the original study really showed such an effect is fiercely debated in today’s literature, but that the presence of an intervention alone can have an effect is fairly well established. That’s the reason why we tend to use what’s called “active controls,” that is, control groups or conditions that also get a comparable intervention or experience. And that's exactly what Henry, Messer, and Nash did: In their study, participants were allocated to either the intervention or an “active control”—a different memory training that was similar in time-commitment and involvement by the children.

Still, in some contexts, the Hawthorne effect may be very difficult to mitigate. In their article in the British Medical Journal , Sedgwick and Greenwood (2015) describe a study testing comparing patient-controlled vs. nurse-controlled administration of pain medication to patients with pain from traumatic injuries.

Which patients fare better: Those that are allowed to control dosage and administration of their pain medication, or those that have medication dosages set and administered by nurses? The answer may surprise you! … Or it probably won’t. Patients who have control over their own medication report better pain management and satisfaction.

But is this because pain management was objectively better and more effective, or because participants had a higher degree of control (and autonomy) over their treatment? They conclude that it’s likely both patients and nurses involved in the study may have been affected by the Hawthorne effect, and that even the “gold standard” of empirical research (double-blinding—more on that in an upcoming post) would likely not have made much of a difference.

Stack Exchange Network

Stack Exchange network consists of 183 Q&A communities including Stack Overflow , the largest, most trusted online community for developers to learn, share their knowledge, and build their careers.

Q&A for work

Connect and share knowledge within a single location that is structured and easy to search.

What is the difference between experiment and experimentation?

If I am correct, both experiment and experimentation exist in English. Which one should I use? Can I say the following two sentences?

• This week, I am conducting new experiments at the laboratory
• This week, I am conducting new experimentations at the laboratory

If the answer is yes, is there any difference between them?

3 Answers 3

Experimentation is the act of doing experiments.

So you would say "I am conducting new experiments at the laboratory"

eg. "not all experimentation is done in a laboratory" means sometimes experiments are done outside the lab.

Yes there is a difference.

Experiment is a test

Experimentation is the act of carrying out a test, or doing tests.

Over 200 experiments are carried out each week at the laboratory. The new car is undergoing experimentation by the expert drivers at the race track.

experimentation

The act, process, or practice of experimenting.

• Children need the opportunity for experimentation.
• Extensive experimentation is needed before new drugs can be sold.
• Experimentation with illegal drugs is dangerous.

experiments

A test under controlled conditions that is made to demonstrate a known truth, examine the validity of a hypothesis, or determine the efficacy of something previously untried.

• Some people believe that experiments on animals should be banned.
• Scientists are conducting/carrying out/doing experiments to test the effectiveness of the new drug.
• I've bought a different kind of coffee this week as an experiment (= in order to see what it is like).
• We can only find the best solution by experiment.

• 2 I think this answer might be slightly confusing. For your example sentences for "experiment", the first and fourth would read fine with "experimentation" (singular), but not the second or third. –  Peter Shor Commented Feb 26, 2012 at 15:45

Not the answer you're looking for? Browse other questions tagged meaning or ask your own question .

• Featured on Meta
• Bringing clarity to status tag usage on meta sites
• We've made changes to our Terms of Service & Privacy Policy - July 2024
• Announcing a change to the data-dump process

Hot Network Questions

• Fitting 10 pieces of pizza in a box
• If Miles doesn’t consider Peter’s actions as hacking, then what does he think Peter is doing to the computer?
• Does gluing two points prevent simple connectedness?
• What's the counterpart to "spheroid" for a circle? There's no "circoid"
• Which point on a hyperbola is closest to a circle
• Name of engineering civil construction device for flattening tarmac
• What is the lesson of the Book of Iyov for the "average" person
• How can I push back on my co-worker's changes that I disagree with?
• Sticker on caption phone says that using the captions can be illegal. Why?
• Display frac form in Forest automatically?
• Do mini-humans need a "real" Saturn V to reach the moon?
• Why are the titles of certain types of works italicized?
• DateTicksFormat -> Automatic different in 14.1 than in 14.0
• When a submarine blows its ballast and rises, where did the energy for the ascent come from?
• "Knocking it out of the park" sports metaphor American English vs British English?
• How to justify a ban on single-deity religions?
• Why if gravity would be higher, designing a fully reusable rocket would be impossible?
• How would I make an agent noun out of the word "autodice -es f" or "autodicia"
• What's the Matter?
• Looking for title of a Ursula K. Le Guin story/novel that features four-way marriage/romantic relationships
• Short story in which the protagonist shrinks to the size of his model train set and is run over by one of his trains
• The McDonald's Option
• Are there any original heat shield tiles on any of the retired space shuttles that flew to space?
• Can the speed of light inhibit the synchronisation of a power grid?

An experiment is a set of actions designed to test a hypothesis. Experimentation is the key step in the Scientific Method because it provides tangible proof that a hypothesis is (probably) true or false.

Why experimentation is important

The Scientific Method

1. Observe a phenomenon that has no good explanation.

2. Formulate a hypothesis.

3. Design an experiment(s) to test the hypothesis.

4. Perform the experiment(s).

5. Accept, reject, or modify the hypothesis.

Once you understand the power of experimentation, and then pause to look at how much experimentation is behind most environmental sustainability problem solving proposals, you will be horrified. There's nearly none. Instead, nearly every article, book, and media appearance is based on the intuitive conclusions of its author. Should we pursue a Global Marshall Plan, as Al Gore argued in Earth in the Balance ? Or should we restructure society along the lines of what Natural Capitalism , by Hawken, Lovins, and Lovins, suggested? Or perhaps we should listen to those promoting sustainable development? Or what about Lester Brown's Eco-Economy , or Maurice Strong's Where on Earth Are We Going? , or Only One Earth: The care and maintenance of a small planet , by Barbara Ward and Rene Dubos? Which course is the one we should take? We can't take them all, because they differ.

The general root causes of a problem are the same no matter who performs the analysis. Thus effective solutions should be about the same. Whenever you encounter a gaggle of solutions that vary wildly, you can be certain that either all or all but one did not use root cause analysis .

What is the future of environmentalism?

Faced with an endless multitude of competing solutions like those listed above, what should we do?

That's the same question science faced for thousands of years: How can we determine what is truth and what it not? Science and scientists were totally unable to answer that question until recently, when the Scientific Method was perfected in the 17th century. After that the way forward was so clear, and so much easier to find, that the speed of scientific progress increased over a hundredfold.

The same could happen to the environmental movement if it changed from intuition to experimentation. The hypotheses to be tested all follow the same pattern: We should do so-and-so to solve this part of the problem.

Any environmentalist who is promoting a solution that is not based on formal analysis and experimentation is exactly where scientists were before they began using the Scientific Method. There were alchemists and quacks.

The right process, true analysis, and heavy experimentation lie at the heart of all efforts to solve extremely difficult problems. The ideas at Thwink.org are no exception. As promising as they may appear to be, they will never amount to much until they go through the Scientific Method's cycle of hypothesis, experimentation, and refinement of the hypothesis.

For more on experimentation, see the Wikipedia entries on experiment and critical experiment .

According to our good friend Wikipedia , “In the sciences, an experimentum crucis (English: crucial experiment or critical experiment) is an experiment capable of decisively determining whether or not a particular hypothesis or theory is superior to all other hypotheses or theories whose acceptance is currently widespread in the scientific community. In particular, such an experiment must typically be able to produce a result that rules out all other hypotheses or theories if true, thereby demonstrating that under the conditions of the experiment (i.e., under the same external circumstances and for the same "input variables" within the experiment), those hypotheses and theories are proven false but the experimenter's hypothesis is not ruled out.”

The biggest problem of the 20 th century was the environmental sustainability problem. The biggest experiment of that century was the hypothesis that popular solutions, which were all about the same from the perspective of Classic Activism , would solve the problem. They did not. The experiment showed the hypothesis to be false. It was the experimentum crucis of the 20 th century.

We are now well into the 21 st century. The same problem looms dead ahead as the world's biggest problem.

What solution strategy will the world's decision makers try this time, in their second experimentum crucis in a row?

Start your reading here:

Mastering the Science of Striking at the Root

Analysis is the breaking down of a problem into smaller easier to solve problems. Exactly how this is done determines the strength of your analysis.

You will see powerful techniques used in this analysis that are missing from what mainstream environmentalism has tried. This explains why a different outcome can be expected.

The key techniques are proper subproblem decomposition and root cause analysis .

The analysis was performed over a seven year period from 2003 to 2010. The results are summarized in the Summary of Analysis Results , the top of which is shown below:

Click on the table for the full table and a high level discussion of analysis results.

This is the solution causal chain present in all problems. Popular approaches to solving the sustainability problem see only what's obvious: the black arrows. This leads to using superficial solutions to push on low leverage points to resolve intermediate causes .

Popular solutions are superficial because they fail to see into the fundamental layer, where the complete causal chain runs to root causes . It's an easy trap to fall into because it intuitively seems that popular solutions like renewable energy and strong regulations should solve the sustainability problem. But they can't, because they don't resolve the root causes.

In the analytical approach, root cause analysis penetrates the fundamental layer to find the well hidden red arrow. Further analysis finds the blue arrow. Fundamental solution elements are then developed to create the green arrow which solves the problem. For more see Causal Chain in the glossary.

First the analysis divided the sustainability problem into four subproblems. Then each subproblem was individually analyzed. For an overview see The Four Subproblems of the Sustainability Problem .

This is no different from what the ancient Romans did. It’s a strategy of divide and conquer. Subproblems like these are several orders of magnitude easier to solve because you are no longer trying (in vain) to solve them simultaneously without realizing it. This strategy has changed millions of other problems from insolvable to solvable, so it should work here too.

For example, multiplying 222 times 222 in your head is for most of us impossible. But doing it on paper, decomposing the problem into nine cases of 2 times 2 and then adding up the results, changes the problem from insolvable to solvable.

Change resistance is the tendency for a system to resist change even when a surprisingly large amount of force is applied.

Overcoming change resistance is the crux of the problem, because if the system is resisting change then none of the other subproblems are solvable. Therefore this subproblem must be solved first. Until it is solved, effort to solve the other three subproblems is largely wasted effort.

The root cause of successful change resistance appears to be effective deception in the political powerplace. Too many voters and politicians are being deceived into thinking sustainability is a low priority and need not be solved now.

The high leverage point for resolving the root cause is to raise general ability to detect political deception. We need to inoculate people against deceptive false memes because once people are infected by falsehoods, it’s very hard to change their minds to see the truth.

Life form improper coupling occurs when two social life forms are not working together in harmony.

In the sustainability problem, large for-profit corporations are not cooperating smoothly with people. Instead, too many corporations are dominating political decision making to their own advantage, as shown by their strenuous opposition to solving the environmental sustainability problem.

The root cause appears to be mutually exclusive goals. The goal of the corporate life form is maximization of profits, while the goal of the human life form is optimization of quality of life, for those living and their descendents. These two goals cannot be both achieved in the same system. One side will win and the other side will lose. Guess which side is losing?

The high leverage point for resolving the root cause follows easily. If the root cause is corporations have the wrong goal, then the high leverage point is to reengineer the modern corporation to have the right goal.

Solution model drift occurs when a problem evolves and its solution model doesn’t keep up. The model “drifts” away from what’s needed to keep the problem solved.

The world’s solution model for solving important problems like sustainability, recurring wars, recurring recessions, excessive economic inequality, and institutional poverty has drifted so far it’s unable to solve the problem.

The root cause appears to be low quality of governmental political decisions. Various steps in the decision making process are not working properly, resulting in inability to proactively solve many difficult problems.

This indicates low decision making process maturity. The high leverage point for resolving the root cause is to raise the maturity of the political decision making process.

In the environmental proper coupling subproblem the world’s economic system is improperly coupled to the environment. Environmental impact from economic system growth has exceeded the capacity of the environment to recycle that impact.

This subproblem is what the world sees as the problem to solve. The analysis shows that to be a false assumption, however. The change resistance subproblem must be solved first.

The root cause appears to be high transaction costs for managing common property (like the air we breath). This means that presently there is no way to manage common property efficiently enough to do it sustainably.

The high leverage point for resolving the root cause is to allow new types of social agents (such as new types of corporations) to appear, in order to radically lower transaction costs.

There must be a reason popular solutions are not working.

Given the principle that all causal problems arise from their root causes, the reason popular solutions are not working (after over 40 years of millions of people trying) is popular solutions do not resolve root causes.

This is Thwink.org’s most fundamental insight.

Using the results of the analysis as input, 12 solutions elements were developed. Each resolves a specific root cause and thus solves one of the four subproblems, as shown below:

Click on the table for a high level discussion of the solution elements and to learn how you can hit the bullseye.

The solutions you are about to see differ radically from popular solutions, because each resolves a specific root cause for a single subproblem. The right subproblems were found earlier in the analysis step, which decomposed the one big Gordian Knot of a problem into The Four Subproblems of the Sustainability Problem .

Everything changes with a root cause resolution approach. You are no longer firing away at a target you can’t see. Once the analysis builds a model of the problem and finds the root causes and their high leverage points, solutions are developed to push on the leverage points.

Because each solution is aimed at resolving a specific known root cause, you can't miss. You hit the bullseye every time. It's like shooting at a target ten feet away. The bullseye is the root cause. That's why Root Cause Analysis is so fantastically powerful.

Nine Sample Solution Elements

1. Freedom from Falsehood

2. The Truth Test

3. Politician Truth Ratings

4. Politician Corruption Ratings

5. No Servant Secrets

6. Corporation 2.0 Suffix

7. Servant Responsibility Ratings

8. Sustainability Index

9. Quality of Life Index

The high leverage point for overcoming change resistance is to raise general ability to detect political deception. We have to somehow make people truth literate so they can’t be fooled so easily by deceptive politicians.

This will not be easy. Overcoming change resistance is the crux of the problem and must be solved first, so it takes nine solution elements to solve this subproblem. The first is the key to it all.

In this subproblem the analysis found that two social life forms, large for-profit corporations and people, have conflicting goals. The high leverage point is correctness of goals for artificial life forms. Since the one causing the problem right now is Corporatis profitis , this means we have to reengineer the modern corporation to have the right goal.

Corporations were never designed in a comprehensive manner to serve the people. They evolved. What we have today can be called Corporation 1.0. It serves itself. What we need instead is Corporation 2.0. This life form is designed to serve people rather than itself. Its new role will be that of a trusted servant whose goal is providing the goods and services needed to optimize quality of life for people in a sustainable manner.

Solution element: Corporation 2.0

What’s drifted too far is the decision making model that governments use to decide what to do. It’s incapable of solving the sustainability problem.

The high leverage point is to greatly improve the maturity of the political decision making process. Like Corporation 1.0, the process was never designed. It evolved. It’s thus not quite what we want.

The solution works like this: Imagine what it would be like if politicians were rated on the quality of their decisions. They would start competing to see who could improve quality of life and the common good the most. That would lead to the most pleasant Race to the Top the world has ever seen.

Solution element: Politician Decision Ratings

Presently the world’s economic system is improperly coupled to the environment. The high leverage point is allow new types of social agents to appear to radically reduce the cost of managing the sustainability problem.

This can be done with non-profit stewardship corporations. Each steward would have the goal of sustainably managing some portion of the sustainability problem. Like the way corporations charge prices for their goods and services, stewards would charge fees for ecosystem service use. The income goes to solving the problem.

Corporations gave us the Industrial Revolution. That revolution is incomplete until stewards give us the Sustainability Revolution.

Solution element: Common Property Rights

Cutting Through Complexity: The Engineer’s Guide to Solving Difficult Social Problems with Root Cause Analysis

This presents our research results, including SIP, analysis of the environmental sustainability problem, and twelve sample solution elements.

The Dueling Loops of the Political Powerplace: Why Progressives Are Stymied and How They Can Find Their Way Again

This analyzes the world’s standard political system and explains why it’s operating for the benefit of special interests instead of the common good. Several sample solutions are presented to help get you thwinking.

Change Resistance as the Crux (journal paper)

Solving Problems with Root Cause Analysis (journal paper)

Democratic Backsliding (working paper)

Striking at the Root with Common Property Rights

Preventing the Death of Democracy

The Trump Phenomenon

The Powell Memo

What Is an Analytical Approach?

Root Cause Analysis: How It Works at Thwink.org

Bridging the Sustainability Gap with Common Property Rights

It's best to start with the first one and watch them all in sequence.

1. Overview of the Dueling Loops , 11 min

Part 1. Basic Concepts of Systems Thinking and the Problem

2. Discovery of the Sustainability Problem by LTG Project , 6 min 3. The Basic Concept of Feedback Loops, with Pop Growth , 9 min 4. How Simulation Models Work, with Pop Growth , 10 min 5. The Importance of Structural Thinking, 3 types , 8 min

Part 2. Deriving the Dueling Loops Shape from Past System Behavior

6. What Jared Diamond’s Collapse Book Attempted to Do , 6 min 7. Extracting the Competitive Spiral from Collapse , 8 min 8. The Two Fundamental Loops of All Political Systems , 5 min 9. The Four Loop Model of Why Some Societies Collapsed , 7 min 10. The Basic Dueling Loops Shape , 15 min

Part 3. How the Basic Dueling Loops Simulation Model Works

11. The Race to the Bottom Simulation Model , 6 min 12. The Five Main Types of Political Deception , 18 min

The Democracy in Crisis Film Series

Introduction to the WorldChange Model , 27 min

Adding Change Resistance to IGMs , 29 min

Part 1. Introduction to Common Property Rights , 15 min

Part 2. The 7 Components of Common Property Rights , 23 min

Truth or Deception , 10 min

The Progressive Paradox Film , 123 min

Introduction to Analytical Activism , 48 min

Abstraction

Agent Based Modeling

Analytical Activism

Analytical Approach

Analytical Method

Best Practice

Broken Political System Problem

Causal Chain

Causal Loop Diagram

Change Resistance

Classic Activism

Competition

Competitive Advantage

Competitive Exclusion Principle

Complex Social System

Cooperation

Cycle of Acceptance

DISMALL Problems

Dueling Loops

Economic Sustainability

Emergent Behavior

Environmental Sustainability

Environmentalism 2.0

Event Oriented Thinking

Feedback Loop

Fundamental Attribution Error

Fundamental Solution

Intermediate Cause

Intuitive Process Trap

Law of Root Causes

Laws of Root Cause Analysis

Leverage Point

Malthusian Trap

MECE Issue Trees

Model Based Analysis

Model Crisis

Model Drift

Model Revolution

More of the Truth

New Dominant Life Form

Normal Science

Paradigm Change

Principle of Cumulative Adv.

Process Driven Problem Solving

Proper Coupling

Root Cause Analysis

Scientific Method

Social Agent

Social Force Diagrams

Social Sustainability

Superficial Solution

Sustainability

System Dynamics

System Improvement Process

Systems Thinking

Three Pillars of Sustainability

The glossary is the foundation for the entire website. It defines the conceptual framework required to "move toward higher levels" of thinking.

Meet the Thwinkers

How You Can Help

What Does Thwink Have to Offer?

Democratic Backsliding (active)

Politician Truth Ratings (inactive)

Atlanta Analytical Activists (inactive)

The Forum (inactive)

The World of Simulation

About Thwink.org

One way to get started is The Common Property Rights Project .

This can be done by switching to Root Cause Analysis , which will lead to Environmentalism 2.0 .