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Experiment Definition in Science – What Is a Science Experiment?

In science, an experiment is simply a test of a hypothesis in the scientific method . It is a controlled examination of cause and effect. Here is a look at what a science experiment is (and is not), the key factors in an experiment, examples, and types of experiments.

Experiment Definition in Science

By definition, an experiment is a procedure that tests a hypothesis. A hypothesis, in turn, is a prediction of cause and effect or the predicted outcome of changing one factor of a situation. Both the hypothesis and experiment are components of the scientific method. The steps of the scientific method are:

• Make observations.
• Ask a question or identify a problem.
• State a hypothesis.
• Perform an experiment that tests the hypothesis.
• Based on the results of the experiment, either accept or reject the hypothesis.
• Draw conclusions and report the outcome of the experiment.

Key Parts of an Experiment

The two key parts of an experiment are the independent and dependent variables. The independent variable is the one factor that you control or change in an experiment. The dependent variable is the factor that you measure that responds to the independent variable. An experiment often includes other types of variables , but at its heart, it’s all about the relationship between the independent and dependent variable.

Examples of Experiments

Fertilizer and plant size.

For example, you think a certain fertilizer helps plants grow better. You’ve watched your plants grow and they seem to do better when they have the fertilizer compared to when they don’t. But, observations are only the beginning of science. So, you state a hypothesis: Adding fertilizer increases plant size. Note, you could have stated the hypothesis in different ways. Maybe you think the fertilizer increases plant mass or fruit production, for example. However you state the hypothesis, it includes both the independent and dependent variables. In this case, the independent variable is the presence or absence of fertilizer. The dependent variable is the response to the independent variable, which is the size of the plants.

Now that you have a hypothesis, the next step is designing an experiment that tests it. Experimental design is very important because the way you conduct an experiment influences its outcome. For example, if you use too small of an amount of fertilizer you may see no effect from the treatment. Or, if you dump an entire container of fertilizer on a plant you could kill it! So, recording the steps of the experiment help you judge the outcome of the experiment and aid others who come after you and examine your work. Other factors that might influence your results might include the species of plant and duration of the treatment. Record any conditions that might affect the outcome. Ideally, you want the only difference between your two groups of plants to be whether or not they receive fertilizer. Then, measure the height of the plants and see if there is a difference between the two groups.

Salt and Cookies

You don’t need a lab for an experiment. For example, consider a baking experiment. Let’s say you like the flavor of salt in your cookies, but you’re pretty sure the batch you made using extra salt fell a bit flat. If you double the amount of salt in a recipe, will it affect their size? Here, the independent variable is the amount of salt in the recipe and the dependent variable is cookie size.

Test this hypothesis with an experiment. Bake cookies using the normal recipe (your control group ) and bake some using twice the salt (the experimental group). Make sure it’s the exact same recipe. Bake the cookies at the same temperature and for the same time. Only change the amount of salt in the recipe. Then measure the height or diameter of the cookies and decide whether to accept or reject the hypothesis.

Examples of Things That Are Not Experiments

Based on the examples of experiments, you should see what is not an experiment:

• Making observations does not constitute an experiment. Initial observations often lead to an experiment, but are not a substitute for one.
• Making a model is not an experiment.
• Neither is making a poster.
• Just trying something to see what happens is not an experiment. You need a hypothesis or prediction about the outcome.
• Changing a lot of things at once isn’t an experiment. You only have one independent and one dependent variable. However, in an experiment, you might suspect the independent variable has an effect on a separate. So, you design a new experiment to test this.

Types of Experiments

There are three main types of experiments: controlled experiments, natural experiments, and field experiments,

• Controlled experiment : A controlled experiment compares two groups of samples that differ only in independent variable. For example, a drug trial compares the effect of a group taking a placebo (control group) against those getting the drug (the treatment group). Experiments in a lab or home generally are controlled experiments
• Natural experiment : Another name for a natural experiment is a quasi-experiment. In this type of experiment, the researcher does not directly control the independent variable, plus there may be other variables at play. Here, the goal is establishing a correlation between the independent and dependent variable. For example, in the formation of new elements a scientist hypothesizes that a certain collision between particles creates a new atom. But, other outcomes may be possible. Or, perhaps only decay products are observed that indicate the element, and not the new atom itself. Many fields of science rely on natural experiments, since controlled experiments aren’t always possible.
• Field experiment : While a controlled experiments takes place in a lab or other controlled setting, a field experiment occurs in a natural setting. Some phenomena cannot be readily studied in a lab or else the setting exerts an influence that affects the results. So, a field experiment may have higher validity. However, since the setting is not controlled, it is also subject to external factors and potential contamination. For example, if you study whether a certain plumage color affects bird mate selection, a field experiment in a natural environment eliminates the stressors of an artificial environment. Yet, other factors that could be controlled in a lab may influence results. For example, nutrition and health are controlled in a lab, but not in the field.
• Bailey, R.A. (2008). Design of Comparative Experiments . Cambridge: Cambridge University Press. ISBN 9780521683579.
• di Francia, G. Toraldo (1981). The Investigation of the Physical World . Cambridge University Press. ISBN 0-521-29925-X.
• Hinkelmann, Klaus; Kempthorne, Oscar (2008). Design and Analysis of Experiments. Volume I: Introduction to Experimental Design (2nd ed.). Wiley. ISBN 978-0-471-72756-9.
• Holland, Paul W. (December 1986). “Statistics and Causal Inference”.  Journal of the American Statistical Association . 81 (396): 945–960. doi: 10.2307/2289064
• Stohr-Hunt, Patricia (1996). “An Analysis of Frequency of Hands-on Experience and Science Achievement”. Journal of Research in Science Teaching . 33 (1): 101–109. doi: 10.1002/(SICI)1098-2736(199601)33:1<101::AID-TEA6>3.0.CO;2-Z

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Meaning of experiment in English

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• Don't forget to try out the equipment before setting up the experiment.
• In the second experiment they obtained a very clear result .
• For the experiment to be valid , it is essential to record the data accurately .
• The experiments were conducted by scientists in New York.
• Our experiment worked better than we could have expected , and soon the baby was happy to sleep in her own bed .
• as an experiment
• background check
• experimental
• experimentally
• experimentation
• experimenter
• put someone/something through their/its paces idiom
• put something to the test idiom
• reinspection
• try something out
• uncheckable
• welfare check
• The young film-makers were given free rein to experiment with new themes and techniques .
• I like to experiment with different light filters on my camera .
• For a while the poet experimented with the idea of chanting his poems to music .
• The artist experimented with different pigments and mediums , often with disastrous results .
• I'd be very nervous about letting a trainee hairdresser experiment with my hair .

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to be doing something good or kind

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• 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]

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[ noun ik- sper - uh -m uh nt ; verb ek- sper - uh -ment ]

a chemical experiment; a teaching experiment; an experiment in living.

a product that is the result of long experiment.

Synonyms: investigation , research

• Obsolete. experience .

verb (used without object)

to experiment with a new procedure.

• a test or investigation, esp one planned to provide evidence for or against a hypothesis: a scientific experiment
• the act of conducting such an investigation or test; experimentation; research

a poetic experiment

• an obsolete word for experience
• intr to make an experiment or experiments

/ ĭk-spĕr ′ ə-mənt /

• A test or procedure carried out under controlled conditions to determine the validity of a hypothesis or make a discovery.
• See Note at hypothesis

Derived Forms

• exˈperiˌmenter , noun

Other Words From

• ex·peri·menter ex·peri·mentor ex·peri·men·tator noun
• preex·peri·ment noun
• proex·peri·ment adjective
• reex·peri·ment verb (used without object) noun
• unex·peri·mented adjective

Word History and Origins

Origin of experiment 1

Synonym Study

Example sentences.

IBM hopes that a platform like RoboRXN could dramatically speed up that process by predicting the recipes for compounds and automating experiments.

The hope there is for improved sensitivity in searches for dark matter or experiments that might reveal some long-sought flaws in our standard model of particle physics.

The experiment represents early progress toward the possible development of an ultra-secure communications network beamed from space.

The new experiment represents, however, the first time scientists have applied machine learning to “validation,” a further step toward confirming results that involves additional statistical calculation.

At first, the sites amounted to experiments on the outer edges of the crypto universe, but in 2020 they have started to attract real money.

To put it rather uncharitably, the USPHS practiced a major dental experiment on a city full of unconsenting subjects.

If the noble experiment of American democracy is to mean anything, it is fidelity to the principle of freedom.

A classroom experiment seeks to demonstrate what it looks like.

This video, courtesy of BuzzFeed, tries a bit of an experiment to get some answers.

In the fall of 1992, Booker became a vegetarian “as an experiment,” he said, “and I was surprised by how much my body took to it.”

With Bacon, experientia does not always mean observation; and may mean either experience or experiment.

I made the experiment two years ago, and all my experience since has corroborated the conclusion then arrived at.

But this is quite enough to justify the inconsiderable expense which the experiment I urge would involve.

He commenced to experiment in electro-pneumatics in the year 1860, and early in 1861 communicated his discoveries to Mr. Barker.

Readers will doubtless be familiar with the well-known experiment illustrating this point.

Related Words

• examination
• experimentation
• observation
• undertaking

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Look up a word, learn it forever.

/ɛkˈspirɪmɪnt/, /ɛkˈspɛrɪmənt/.

Other forms: experiments; experimenting; experimented

If you see your science-loving neighbor headed home with a power cord, a handful of test tubes, a stopwatch, and a bag of potatoes, there’s probably no need to be alarmed. There’s a good chance he’s only conducting an experiment , a scientific test conducted under controlled conditions.

To refer to a scientific test, use the noun experiment . If you want to describe the work done in conducting such a test, experiment will do the trick as well, since it can also act as a verb, as in "scientists experiment with helium." You can also use it more generally to describe trying a new method or idea. For example, you could experiment with a new hairstyle or different routes to get to school or work.

• noun the act of conducting a controlled test or investigation synonyms: experimentation see more see less types: show 4 types... hide 4 types... testing the act of subjecting to experimental test in order to determine how well something works trial and error experimenting until a solution is found Michelson-Morley experiment a celebrated experiment conducted by Albert Michelson and Edward Morley; their failure to detect any influence of the earth's motion on the velocity of light was the starting point for Einstein's theory of relativity control experiment an experiment designed to control for variables affecting the results of another experiment type of: research project , scientific research research into questions posed by scientific theories and hypotheses
• noun the testing of an idea “it was an experiment in living” synonyms: experimentation see more see less types: show 7 types... hide 7 types... pilot experiment a preliminary experiment whose outcome can lead to a more extensive experiment test , trial , trial run , tryout trying something to find out about it field test , field trial a test of the performance of some new product under the conditions in which it will be used alpha test (computer science) a first test of an experimental product (such as computer software) carried out by the developer beta test (computer science) a second test of an experimental product (such as computer software) carried out by an outside organization road test a test to insure that a vehicle is roadworthy trial balloon a test of public opinion type of: enquiry , inquiry , research a search for knowledge
• noun a venture at something new or different “as an experiment he decided to grow a beard” see more see less type of: venture any venturesome undertaking especially one with an uncertain outcome
• verb conduct a test or investigation “We are experimenting with the new drug in order to fight this disease” synonyms: try out try something new, as in order to gain experience see more see less type of: investigate , look into investigate scientifically
• verb try something new, as in order to gain experience “The composer experimented with a new style” synonyms: try out

Vocabulary lists containing experiment

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5.1 Experiment Basics

Learning objectives.

• Explain what an experiment is and recognize examples of studies that are experiments and studies that are not experiments.
• Distinguish between the manipulation of the independent variable and control of extraneous variables and explain the importance of each.
• Recognize examples of confounding variables and explain how they affect the internal validity of a study.

What Is an Experiment?

As we saw earlier in the book, an  experiment  is a type of study designed specifically to answer the question of whether there is a causal relationship between two variables. In other words, whether changes in an independent variable  cause  a change in a dependent variable. Experiments have two fundamental features. The first is that the researchers manipulate, or systematically vary, the level of the independent variable. The different levels of the independent variable are called conditions . For example, in Darley and Latané’s experiment, the independent variable was the number of witnesses that participants believed to be present. The researchers manipulated this independent variable by telling participants that there were either one, two, or five other students involved in the discussion, thereby creating three conditions. For a new researcher, it is easy to confuse  these terms by believing there are three independent variables in this situation: one, two, or five students involved in the discussion, but there is actually only one independent variable (number of witnesses) with three different levels or conditions (one, two or five students). The second fundamental feature of an experiment is that the researcher controls, or minimizes the variability in, variables other than the independent and dependent variable. These other variables are called extraneous variables . Darley and Latané tested all their participants in the same room, exposed them to the same emergency situation, and so on. They also randomly assigned their participants to conditions so that the three groups would be similar to each other to begin with. Notice that although the words  manipulation  and  control  have similar meanings in everyday language, researchers make a clear distinction between them. They manipulate  the independent variable by systematically changing its levels and control  other variables by holding them constant.

Manipulation of the Independent Variable

Again, to  manipulate  an independent variable means to change its level systematically so that different groups of participants are exposed to different levels of that variable, or the same group of participants is exposed to different levels at different times. For example, to see whether expressive writing affects people’s health, a researcher might instruct some participants to write about traumatic experiences and others to write about neutral experiences. As discussed earlier in this chapter, the different levels of the independent variable are referred to as  conditions , and researchers often give the conditions short descriptive names to make it easy to talk and write about them. In this case, the conditions might be called the “traumatic condition” and the “neutral condition.”

Notice that the manipulation of an independent variable must involve the active intervention of the researcher. Comparing groups of people who differ on the independent variable before the study begins is not the same as manipulating that variable. For example, a researcher who compares the health of people who already keep a journal with the health of people who do not keep a journal has not manipulated this variable and therefore has not conducted an experiment. This distinction  is important because groups that already differ in one way at the beginning of a study are likely to differ in other ways too. For example, people who choose to keep journals might also be more conscientious, more introverted, or less stressed than people who do not. Therefore, any observed difference between the two groups in terms of their health might have been caused by whether or not they keep a journal, or it might have been caused by any of the other differences between people who do and do not keep journals. Thus the active manipulation of the independent variable is crucial for eliminating potential alternative explanations for the results.

Of course, there are many situations in which the independent variable cannot be manipulated for practical or ethical reasons and therefore an experiment is not possible. For example, whether or not people have a significant early illness experience cannot be manipulated, making it impossible to conduct an experiment on the effect of early illness experiences on the development of hypochondriasis. This caveat does not mean it is impossible to study the relationship between early illness experiences and hypochondriasis—only that it must be done using nonexperimental approaches. We will discuss this type of methodology in detail later in the book.

Independent variables can be manipulated to create two conditions and experiments involving a single independent variable with two conditions is often referred to as a  single factor two-level design.  However, sometimes greater insights can be gained by adding more conditions to an experiment. When an experiment has one independent variable that is manipulated to produce more than two conditions it is referred to as a single factor multi level design.  So rather than comparing a condition in which there was one witness to a condition in which there were five witnesses (which would represent a single-factor two-level design), Darley and Latané’s used a single factor multi-level design, by manipulating the independent variable to produce three conditions (a one witness, a two witnesses, and a five witnesses condition).

Control of Extraneous Variables

As we have seen previously in the chapter, an  extraneous variable  is anything that varies in the context of a study other than the independent and dependent variables. In an experiment on the effect of expressive writing on health, for example, extraneous variables would include participant variables (individual differences) such as their writing ability, their diet, and their gender. They would also include situational or task variables such as the time of day when participants write, whether they write by hand or on a computer, and the weather. Extraneous variables pose a problem because many of them are likely to have some effect on the dependent variable. For example, participants’ health will be affected by many things other than whether or not they engage in expressive writing. This influencing factor can make it difficult to separate the effect of the independent variable from the effects of the extraneous variables, which is why it is important to  control  extraneous variables by holding them constant.

Extraneous Variables as “Noise”

Extraneous variables make it difficult to detect the effect of the independent variable in two ways. One is by adding variability or “noise” to the data. Imagine a simple experiment on the effect of mood (happy vs. sad) on the number of happy childhood events people are able to recall. Participants are put into a negative or positive mood (by showing them a happy or sad video clip) and then asked to recall as many happy childhood events as they can. The two leftmost columns of  Table 5.1 show what the data might look like if there were no extraneous variables and the number of happy childhood events participants recalled was affected only by their moods. Every participant in the happy mood condition recalled exactly four happy childhood events, and every participant in the sad mood condition recalled exactly three. The effect of mood here is quite obvious. In reality, however, the data would probably look more like those in the two rightmost columns of  Table 5.1 . Even in the happy mood condition, some participants would recall fewer happy memories because they have fewer to draw on, use less effective recall strategies, or are less motivated. And even in the sad mood condition, some participants would recall more happy childhood memories because they have more happy memories to draw on, they use more effective recall strategies, or they are more motivated. Although the mean difference between the two groups is the same as in the idealized data, this difference is much less obvious in the context of the greater variability in the data. Thus one reason researchers try to control extraneous variables is so their data look more like the idealized data in  Table 5.1 , which makes the effect of the independent variable easier to detect (although real data never look quite  that  good).

One way to control extraneous variables is to hold them constant. This technique can mean holding situation or task variables constant by testing all participants in the same location, giving them identical instructions, treating them in the same way, and so on. It can also mean holding participant variables constant. For example, many studies of language limit participants to right-handed people, who generally have their language areas isolated in their left cerebral hemispheres. Left-handed people are more likely to have their language areas isolated in their right cerebral hemispheres or distributed across both hemispheres, which can change the way they process language and thereby add noise to the data.

In principle, researchers can control extraneous variables by limiting participants to one very specific category of person, such as 20-year-old, heterosexual, female, right-handed psychology majors. The obvious downside to this approach is that it would lower the external validity of the study—in particular, the extent to which the results can be generalized beyond the people actually studied. For example, it might be unclear whether results obtained with a sample of younger heterosexual women would apply to older homosexual men. In many situations, the advantages of a diverse sample (increased external validity) outweigh the reduction in noise achieved by a homogeneous one.

Extraneous Variables as Confounding Variables

The second way that extraneous variables can make it difficult to detect the effect of the independent variable is by becoming confounding variables. A confounding variable  is an extraneous variable that differs on average across  levels of the independent variable (i.e., it is an extraneous variable that varies systematically with the independent variable). For example, in almost all experiments, participants’ intelligence quotients (IQs) will be an extraneous variable. But as long as there are participants with lower and higher IQs in each condition so that the average IQ is roughly equal across the conditions, then this variation is probably acceptable (and may even be desirable). What would be bad, however, would be for participants in one condition to have substantially lower IQs on average and participants in another condition to have substantially higher IQs on average. In this case, IQ would be a confounding variable.

To confound means to confuse , and this effect is exactly why confounding variables are undesirable. Because they differ systematically across conditions—just like the independent variable—they provide an alternative explanation for any observed difference in the dependent variable.  Figure 5.1  shows the results of a hypothetical study, in which participants in a positive mood condition scored higher on a memory task than participants in a negative mood condition. But if IQ is a confounding variable—with participants in the positive mood condition having higher IQs on average than participants in the negative mood condition—then it is unclear whether it was the positive moods or the higher IQs that caused participants in the first condition to score higher. One way to avoid confounding variables is by holding extraneous variables constant. For example, one could prevent IQ from becoming a confounding variable by limiting participants only to those with IQs of exactly 100. But this approach is not always desirable for reasons we have already discussed. A second and much more general approach—random assignment to conditions—will be discussed in detail shortly.

Figure 5.1 Hypothetical Results From a Study on the Effect of Mood on Memory. Because IQ also differs across conditions, it is a confounding variable.

Key Takeaways

• An experiment is a type of empirical study that features the manipulation of an independent variable, the measurement of a dependent variable, and control of extraneous variables.
• An extraneous variable is any variable other than the independent and dependent variables. A confound is an extraneous variable that varies systematically with the independent variable.
• Practice: List five variables that can be manipulated by the researcher in an experiment. List five variables that cannot be manipulated by the researcher in an experiment.
• Effect of parietal lobe damage on people’s ability to do basic arithmetic.
• Effect of being clinically depressed on the number of close friendships people have.
• Effect of group training on the social skills of teenagers with Asperger’s syndrome.
• Effect of paying people to take an IQ test on their performance on that test.

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Experimental Method In Psychology

Saul McLeod, PhD

Editor-in-Chief for Simply Psychology

BSc (Hons) Psychology, MRes, PhD, University of Manchester

Saul McLeod, PhD., is a qualified psychology teacher with over 18 years of experience in further and higher education. He has been published in peer-reviewed journals, including the Journal of Clinical Psychology.

Learn about our Editorial Process

Olivia Guy-Evans, MSc

Associate Editor for Simply Psychology

BSc (Hons) Psychology, MSc Psychology of Education

Olivia Guy-Evans is a writer and associate editor for Simply Psychology. She has previously worked in healthcare and educational sectors.

On This Page:

The experimental method involves the manipulation of variables to establish cause-and-effect relationships. The key features are controlled methods and the random allocation of participants into controlled and experimental groups .

What is an Experiment?

An experiment is an investigation in which a hypothesis is scientifically tested. An independent variable (the cause) is manipulated in an experiment, and the dependent variable (the effect) is measured; any extraneous variables are controlled.

An advantage is that experiments should be objective. The researcher’s views and opinions should not affect a study’s results. This is good as it makes the data more valid  and less biased.

There are three types of experiments you need to know:

1. Lab Experiment

A laboratory experiment in psychology is a research method in which the experimenter manipulates one or more independent variables and measures the effects on the dependent variable under controlled conditions.

A laboratory experiment is conducted under highly controlled conditions (not necessarily a laboratory) where accurate measurements are possible.

The researcher uses a standardized procedure to determine where the experiment will take place, at what time, with which participants, and in what circumstances.

Participants are randomly allocated to each independent variable group.

Examples are Milgram’s experiment on obedience and  Loftus and Palmer’s car crash study .

• Strength : It is easier to replicate (i.e., copy) a laboratory experiment. This is because a standardized procedure is used.
• Strength : They allow for precise control of extraneous and independent variables. This allows a cause-and-effect relationship to be established.
• Limitation : The artificiality of the setting may produce unnatural behavior that does not reflect real life, i.e., low ecological validity. This means it would not be possible to generalize the findings to a real-life setting.
• Limitation : Demand characteristics or experimenter effects may bias the results and become confounding variables .

2. Field Experiment

A field experiment is a research method in psychology that takes place in a natural, real-world setting. It is similar to a laboratory experiment in that the experimenter manipulates one or more independent variables and measures the effects on the dependent variable.

However, in a field experiment, the participants are unaware they are being studied, and the experimenter has less control over the extraneous variables .

Field experiments are often used to study social phenomena, such as altruism, obedience, and persuasion. They are also used to test the effectiveness of interventions in real-world settings, such as educational programs and public health campaigns.

An example is Holfing’s hospital study on obedience .

• Strength : behavior in a field experiment is more likely to reflect real life because of its natural setting, i.e., higher ecological validity than a lab experiment.
• Strength : Demand characteristics are less likely to affect the results, as participants may not know they are being studied. This occurs when the study is covert.
• Limitation : There is less control over extraneous variables that might bias the results. This makes it difficult for another researcher to replicate the study in exactly the same way.

3. Natural Experiment

A natural experiment in psychology is a research method in which the experimenter observes the effects of a naturally occurring event or situation on the dependent variable without manipulating any variables.

Natural experiments are conducted in the day (i.e., real life) environment of the participants, but here, the experimenter has no control over the independent variable as it occurs naturally in real life.

Natural experiments are often used to study psychological phenomena that would be difficult or unethical to study in a laboratory setting, such as the effects of natural disasters, policy changes, or social movements.

For example, Hodges and Tizard’s attachment research (1989) compared the long-term development of children who have been adopted, fostered, or returned to their mothers with a control group of children who had spent all their lives in their biological families.

Here is a fictional example of a natural experiment in psychology:

Researchers might compare academic achievement rates among students born before and after a major policy change that increased funding for education.

In this case, the independent variable is the timing of the policy change, and the dependent variable is academic achievement. The researchers would not be able to manipulate the independent variable, but they could observe its effects on the dependent variable.

• Strength : behavior in a natural experiment is more likely to reflect real life because of its natural setting, i.e., very high ecological validity.
• Strength : Demand characteristics are less likely to affect the results, as participants may not know they are being studied.
• Strength : It can be used in situations in which it would be ethically unacceptable to manipulate the independent variable, e.g., researching stress .
• Limitation : They may be more expensive and time-consuming than lab experiments.
• Limitation : There is no control over extraneous variables that might bias the results. This makes it difficult for another researcher to replicate the study in exactly the same way.

Key Terminology

Ecological validity.

The degree to which an investigation represents real-life experiences.

Experimenter effects

These are the ways that the experimenter can accidentally influence the participant through their appearance or behavior.

Demand characteristics

The clues in an experiment lead the participants to think they know what the researcher is looking for (e.g., the experimenter’s body language).

Independent variable (IV)

The variable the experimenter manipulates (i.e., changes) is assumed to have a direct effect on the dependent variable.

Dependent variable (DV)

Variable the experimenter measures. This is the outcome (i.e., the result) of a study.

Extraneous variables (EV)

All variables which are not independent variables but could affect the results (DV) of the experiment. EVs should be controlled where possible.

Confounding variables

Variable(s) that have affected the results (DV), apart from the IV. A confounding variable could be an extraneous variable that has not been controlled.

Random Allocation

Randomly allocating participants to independent variable conditions means that all participants should have an equal chance of participating in each condition.

The principle of random allocation is to avoid bias in how the experiment is carried out and limit the effects of participant variables.

Order effects

Changes in participants’ performance due to their repeating the same or similar test more than once. Examples of order effects include:

(i) practice effect: an improvement in performance on a task due to repetition, for example, because of familiarity with the task;

(ii) fatigue effect: a decrease in performance of a task due to repetition, for example, because of boredom or tiredness.

Scientific Method Example

Illustration by J.R. Bee. ThoughtCo. 

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The scientific method is a series of steps that scientific investigators follow to answer specific questions about the natural world. Scientists use the scientific method to make observations, formulate hypotheses , and conduct scientific experiments .

A scientific inquiry starts with an observation. Then, the formulation of a question about what has been observed follows. Next, the scientist will proceed through the remaining steps of the scientific method to end at a conclusion.

The six steps of the scientific method are as follows:

Observation

The first step of the scientific method involves making an observation about something that interests you. Taking an interest in your scientific discovery is important, for example, if you are doing a science project , because you will want to work on something that holds your attention. Your observation can be of anything from plant movement to animal behavior, as long as it is something you want to know more about.​ This step is when you will come up with an idea if you are working on a science project.

Once you have made your observation, you must formulate a question about what you observed. Your question should summarize what it is you are trying to discover or accomplish in your experiment. When stating your question, be as specific as possible.​ For example, if you are doing a project on plants , you may want to know how plants interact with microbes. Your question could be: Do plant spices inhibit bacterial growth ?

The hypothesis is a key component of the scientific process. A hypothesis is an idea that is suggested as an explanation for a natural event, a particular experience, or a specific condition that can be tested through definable experimentation. It states the purpose of your experiment, the variables used, and the predicted outcome of your experiment. It is important to note that a hypothesis must be testable. That means that you should be able to test your hypothesis through experimentation .​ Your hypothesis must either be supported or falsified by your experiment. An example of a good hypothesis is: If there is a relation between listening to music and heart rate, then listening to music will cause a person's resting heart rate to either increase or decrease.

Once you have developed a hypothesis, you must design and conduct an experiment that will test it. You should develop a procedure that states clearly how you plan to conduct your experiment. It is important you include and identify a controlled variable or dependent variable in your procedure. Controls allow us to test a single variable in an experiment because they are unchanged. We can then make observations and comparisons between our controls and our independent variables (things that change in the experiment) to develop an accurate conclusion.​

The results are where you report what happened in the experiment. That includes detailing all observations and data made during your experiment. Most people find it easier to visualize the data by charting or graphing the information.​

Developing a conclusion is the final step of the scientific method. This is where you analyze the results from the experiment and reach a determination about the hypothesis. Did the experiment support or reject your hypothesis? If your hypothesis was supported, great. If not, repeat the experiment or think of ways to improve your procedure.

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Methodology

• What Is a Controlled Experiment? | Definitions & Examples

What Is a Controlled Experiment? | Definitions & Examples

Published on April 19, 2021 by Pritha Bhandari . Revised on June 22, 2023.

In experiments , researchers manipulate independent variables to test their effects on dependent variables. In a controlled experiment , all variables other than the independent variable are controlled or held constant so they don’t influence the dependent variable.

Controlling variables can involve:

• holding variables at a constant or restricted level (e.g., keeping room temperature fixed).
• measuring variables to statistically control for them in your analyses.
• balancing variables across your experiment through randomization (e.g., using a random order of tasks).

Table of contents

Why does control matter in experiments, methods of control, problems with controlled experiments, other interesting articles, frequently asked questions about controlled experiments.

Control in experiments is critical for internal validity , which allows you to establish a cause-and-effect relationship between variables. Strong validity also helps you avoid research biases , particularly ones related to issues with generalizability (like sampling bias and selection bias .)

• Your independent variable is the color used in advertising.
• Your dependent variable is the price that participants are willing to pay for a standard fast food meal.

Extraneous variables are factors that you’re not interested in studying, but that can still influence the dependent variable. For strong internal validity, you need to remove their effects from your experiment.

• Design and description of the meal,
• Study environment (e.g., temperature or lighting),
• Participant’s frequency of buying fast food,
• Participant’s familiarity with the specific fast food brand,
• Participant’s socioeconomic status.

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You can control some variables by standardizing your data collection procedures. All participants should be tested in the same environment with identical materials. Only the independent variable (e.g., ad color) should be systematically changed between groups.

Other extraneous variables can be controlled through your sampling procedures . Ideally, you’ll select a sample that’s representative of your target population by using relevant inclusion and exclusion criteria (e.g., including participants from a specific income bracket, and not including participants with color blindness).

By measuring extraneous participant variables (e.g., age or gender) that may affect your experimental results, you can also include them in later analyses.

After gathering your participants, you’ll need to place them into groups to test different independent variable treatments. The types of groups and method of assigning participants to groups will help you implement control in your experiment.

Control groups

Controlled experiments require control groups . Control groups allow you to test a comparable treatment, no treatment, or a fake treatment (e.g., a placebo to control for a placebo effect ), and compare the outcome with your experimental treatment.

You can assess whether it’s your treatment specifically that caused the outcomes, or whether time or any other treatment might have resulted in the same effects.

To test the effect of colors in advertising, each participant is placed in one of two groups:

• A control group that’s presented with red advertisements for a fast food meal.
• An experimental group that’s presented with green advertisements for the same fast food meal.

Random assignment

To avoid systematic differences and selection bias between the participants in your control and treatment groups, you should use random assignment .

This helps ensure that any extraneous participant variables are evenly distributed, allowing for a valid comparison between groups .

Random assignment is a hallmark of a “true experiment”—it differentiates true experiments from quasi-experiments .

Masking (blinding)

Masking in experiments means hiding condition assignment from participants or researchers—or, in a double-blind study , from both. It’s often used in clinical studies that test new treatments or drugs and is critical for avoiding several types of research bias .

Sometimes, researchers may unintentionally encourage participants to behave in ways that support their hypotheses , leading to observer bias . In other cases, cues in the study environment may signal the goal of the experiment to participants and influence their responses. These are called demand characteristics . If participants behave a particular way due to awareness of being observed (called a Hawthorne effect ), your results could be invalidated.

Using masking means that participants don’t know whether they’re in the control group or the experimental group. This helps you control biases from participants or researchers that could influence your study results.

You use an online survey form to present the advertisements to participants, and you leave the room while each participant completes the survey on the computer so that you can’t tell which condition each participant was in.

Although controlled experiments are the strongest way to test causal relationships, they also involve some challenges.

Difficult to control all variables

Especially in research with human participants, it’s impossible to hold all extraneous variables constant, because every individual has different experiences that may influence their perception, attitudes, or behaviors.

But measuring or restricting extraneous variables allows you to limit their influence or statistically control for them in your study.

Risk of low external validity

Controlled experiments have disadvantages when it comes to external validity —the extent to which your results can be generalized to broad populations and settings.

The more controlled your experiment is, the less it resembles real world contexts. That makes it harder to apply your findings outside of a controlled setting.

There’s always a tradeoff between internal and external validity . It’s important to consider your research aims when deciding whether to prioritize control or generalizability in your experiment.

If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

• Student’s  t -distribution
• Normal distribution
• Null and Alternative Hypotheses
• Chi square tests
• Confidence interval
• Quartiles & Quantiles
• Cluster sampling
• Stratified sampling
• Data cleansing
• Reproducibility vs Replicability
• Peer review
• Prospective cohort study

Research bias

• Implicit bias
• Cognitive bias
• Placebo effect
• Hawthorne effect
• Hindsight bias
• Affect heuristic
• Social desirability bias

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In a controlled experiment , all extraneous variables are held constant so that they can’t influence the results. Controlled experiments require:

• A control group that receives a standard treatment, a fake treatment, or no treatment.
• Random assignment of participants to ensure the groups are equivalent.

Depending on your study topic, there are various other methods of controlling variables .

An experimental group, also known as a treatment group, receives the treatment whose effect researchers wish to study, whereas a control group does not. They should be identical in all other ways.

Experimental design means planning a set of procedures to investigate a relationship between variables . To design a controlled experiment, you need:

• A testable hypothesis
• At least one independent variable that can be precisely manipulated
• At least one dependent variable that can be precisely measured

When designing the experiment, you decide:

• How you will manipulate the variable(s)
• How you will control for any potential confounding variables
• How many subjects or samples will be included in the study
• How subjects will be assigned to treatment levels

Experimental design is essential to the internal and external validity of your experiment.

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Science experiments you can do at home!  Explore an ever growing list of hundreds of fun and easy science experiments. Have fun trying these experiments at home or use them for science fair project ideas. Explore experiments by category, newest experiments, most popular experiments, easy at home experiments, or simply scroll down this page for tons of awesome experiment ideas!

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experimental

Definition of experimental

• developmental

Examples of experimental in a Sentence

These examples are programmatically compiled from various online sources to illustrate current usage of the word 'experimental.' Any opinions expressed in the examples do not represent those of Merriam-Webster or its editors. Send us feedback about these examples.

Word History

Middle English, borrowed from Medieval Latin experīmentālis, from Latin experīmentum "testing, experience, proof" + -ālis -al entry 1 — more at experiment entry 1

15th century, in the meaning defined at sense 1

Phrases Containing experimental

• pre - experimental

Articles Related to experimental

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In scientific reasoning, they're two completely different things

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experimental design

Cite this Entry

“Experimental.” Merriam-Webster.com Dictionary , Merriam-Webster, https://www.merriam-webster.com/dictionary/experimental. Accessed 22 Aug. 2024.

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Kids definition of experimental, medical definition, medical definition of experimental, more from merriam-webster on experimental.

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Experiment vs Experimentation - What's the difference?

As nouns the difference between experiment and experimentation, as a verb experiment, related terms, derived terms, experimentation.

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Definition of experiment noun from the Oxford Advanced American Dictionary

• formulate/advance a theory/hypothesis
• build/construct/create/develop a simple/theoretical/mathematical model
• develop/establish/provide/use a theoretical/conceptual framework/an algorithm
• advance/argue/develop the thesis that…
• explore an idea/a concept/a hypothesis
• make a prediction/an inference
• base a prediction/your calculations on something
• investigate/evaluate/accept/challenge/reject a theory/hypothesis/model
• design an experiment/a questionnaire/a study/a test
• do research/an experiment/an analysis
• make observations/calculations
• take/record measurements
• carry out/conduct/perform an experiment/a test/a longitudinal study/observations/clinical trials
• run an experiment/a simulation/clinical trials
• repeat an experiment/a test/an analysis
• replicate a study/the results/the findings
• observe/study/examine/investigate/assess a pattern/a process/a behavior
• fund/support the research/project/study
• seek/provide/get/secure funding for research
• collect/gather/extract data/information
• yield data/evidence/similar findings/the same results
• analyze/examine the data/soil samples/a specimen
• consider/compare/interpret the results/findings
• fit the data/model
• confirm/support/verify a prediction/a hypothesis/the results/the findings
• prove a conjecture/hypothesis/theorem
• draw/make/reach the same conclusions
• read/review the records/literature
• describe/report an experiment/a study
• present/publish/summarize the results/findings
• present/publish/read/review/cite a paper in a scientific journal

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Find the answers with Practical English Usage online, your indispensable guide to problems in English.

• 2 a new activity, idea, or method that you try out to see what happens or what effect it has I've never cooked this before so it's an experiment. The system was installed four years ago as an experiment. experiment in something the country's brief experiment in democracy

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Summer holiday science: turn your home into a lab with these three easy experiments

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Disclosure statement

Audrey O'Grady receives funding from Science Foundation Ireland. She is affiliated with Department of Biological Sciences, University of Limerick.

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Many people think science is difficult and needs special equipment, but that’s not true.

Science can be explored at home using everyday materials. Everyone, especially children, naturally ask questions about the world around them, and science offers a structured way to find answers.

Misconceptions about the difficulty of science often stem from a lack of exposure to its fun and engaging side. Science can be as simple as observing nature, mixing ingredients or exploring the properties of objects. It’s not just for experts in white coats, but for everyone.

Don’t take my word for it. Below are three experiments that can be done at home with children who are primary school age and older.

Extract DNA from bananas

DNA is all the genetic information inside cells. Every living thing has DNA, including bananas.

Did you know you can extract DNA from banana cells?

What you need: ¼ ripe banana, Ziploc bag, salt, water, washing-up liquid, rubbing alcohol (from a pharmacy), coffee filter paper, stirrer.

What you do:

Place a pinch of salt into about 20ml of water in a cup.

Add the salty water to the Ziploc bag with a quarter of a banana and mash the banana up with the salty water inside the bag, using your hands. Mashing the banana separates out the banana cells. The salty water helps clump the DNA together.

Once the banana is mashed up well, pour the banana and salty water into a coffee filter (you can lay the filter in the cup you used to make the salty water). Filtering removes the big clumps of banana cells.

Once a few ml have filtered out, add a drop of washing-up liquid and swirl gently. Washing-up liquid breaks down the fats in the cell membranes which makes the DNA separate from the other parts of the cell.

Slowly add some rubbing alcohol (about 10ml) to the filtered solution. DNA is insoluble in alcohol, therefore the DNA will clump together away from the alcohol and float, making it easy to see.

DNA will start to precipitate out looking slightly cloudy and stringy. What you’re seeing is thousands of DNA strands – the strands are too small to be seen even with a normal microscope. Scientists use powerful equipment to see individual strands.

Learn how plants ‘drink’ water

What you need: celery stalks (with their leaves), glass or clear cup, water, food dye, camera.

• Fill the glass ¾ full with water and add 10 drops of food dye.
• Place a celery stalk into the glass of coloured water. Take a photograph of the celery.
• For two to three days, photograph the celery at the same time every day. Make sure you take a photograph at the very start of the experiment.

What happens and why?

All plants, such as celery, have vertical tubes that act like a transport system. These narrow tubes draw up water using a phenomenon known as capillarity.

Imagine you have a thin straw and you dip it into a glass of water. Have you ever noticed how the water climbs up the straw a little bit, even though you didn’t suck on it? This is because of capillarity.

In plants, capillarity helps move water from the roots to the leaves. Plants have tiny tubes inside them, like thin straws, called capillaries. The water sticks to the sides of these tubes and climbs up. In your experiment, you will see the food dye in the water make its way to the leaves.

Build a balloon-powered racecar

What you need: tape, scissors, two skewers, cardboard, four bottle caps, one straw, one balloon.

• Cut the cardboard to about 10cm long and 5cm wide. This will form the base of your car.
• Make holes in the centre of four bottle caps. These are your wheels.
• To make the axles insert the wooden skewers through the holes in the cap. You will need to cut the skewers to fit the width of the cardboard base, but leave room for the wheels.
• Secure the wheels to the skewers with tape.
• Attach the axles to the underside of the car base with tape, ensuring the wheels can spin freely.
• Insert a straw into the opening of a balloon and secure it with tape, ensuring there are no air leaks.
• Attach the other end of the straw to the top of the car base, positioning it so the balloon can inflate and deflate towards the back of the car. Secure the straw with tape.
• Inflate the balloon through the straw, pinch the straw to hold the air, place the car on a flat surface, then release the straw.

The inflated balloon stores potential energy when blown up. When the air is released, Newton’s third law of motion kicks into gear: for every action, there is an equal and opposite reaction.

As the air rushes out of the balloon (action), it pushes the car in the opposite direction (reaction). The escaping air propels the car forward, making it move across the surface.

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Eagles end experiment with ex-Jets' $24 million tight end

His Philadelphia Eagles ' tenure has essentially ended before it started.

In a move confirmed by the team, the Eagles have released former New York Jets ' tight end CJ Uzomah from their 90-man roster. He was coming off an August 15 preseason performance that consisted of 18 offensive snaps and no targets. Another former Jet, second-year undrafted tight end EJ Jenkins, upstaged Uzomah by catching all five of his targets against the New England Patriots.

The 31-year-old Uzomah signed a one-year contract with Philadelphia in early April after the Jets sent him packing at the start of the new league year despite one season remaining on the three-year deal he inked as a free agent in 2022.

Uzomah managed only 29 receptions over 27 games as a Jet after receiving a contract worth $24 million total ($8 million average annual value). The former fifth-round draft pick ended the 2023 campaign on Injured Reserve.

The Jets lost the veteran tight end in the first quarter of a December 3 home game against the Atlanta Falcons . Uzomah suffered an MCL injury while run blocking and was carted to the locker room. He totaled 239 offensive snaps in 2023, making eight catches for 58 yards and one touchdown.

His best pro season as a pass-catcher came with the Cincinnati Bengals in 2021. Over 16 starts that year, Uzomah secured 49 of 63 targets for 493 yards and five touchdowns. He spent the first seven years of his career in Cincinnati.

Over 106 career games, Uzomah has accounted for a 9.8 yards per catch average and 16 TD receptions.

More New York Jets News:

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•  Jets' former $45 million man signs with Dallas Cowboys

•  Robert Saleh rules out three injured defenders for rest of preseason

•  Jets' prime free-agent addition trending toward Week 1 availability

Ralph Ventre, a former college football conference administrator, brings 20 years of media experience to the New York Jets beat. Prior to concentrating on Gang Green, he covered the NCAA DI Football Championship Subdivision for NFL Draft Bible on Sports Illustrated’s FanNation. He is a member of the Pro Football Writers of America.

Maxwell's Thought Experiment 4+

The paradox of maxwell's demon, ventura educational systems, designed for ipad, screenshots, description.

Discover Thermodynamics with Our Interactive Educational App Discover Thermodynamics with Our Interactive Educational App: Unveil the Mysteries of Maxwell's Demon! Unlock the secrets of thermodynamics with our innovative educational app, designed to make learning science engaging and interactive! Dive into the fascinating world of physics through a thought experiment that has shaped our understanding of how our universe works. This app provides an in-depth exploration of Maxwell's Demon, a renowned thought experiment conceived by Scottish physicist James Clerk Maxwell. What is Maxwell's Demon? Maxwell's Demon is a mind-bending concept that challenges the principles of thermodynamics. Imagine a demon that can sort fast-moving (hot) and slow-moving (cold) molecules through a door between two chambers, seemingly decreasing entropy and violating the second law of thermodynamics. This paradoxical scenario highlights the intricacies and surprises in the study of thermodynamic systems. Why Our App Stands Out: Interactive Simulations: Experience Maxwell's Demon in action with hands-on simulations that bring this thought experiment to life. Watch how the demon selectively allows molecules to pass through the door, and understand why this challenges traditional thermodynamic laws.
 Engaging Lessons: Our app breaks down complex scientific concepts into easy-to-understand lessons. Learn about entropy, the second law of thermodynamics, and the significance of thought experiments in physics.
 Quizzes and Challenges: Test your knowledge with interactive quizzes and challenges designed to reinforce learning. Earn certificates as you progress through the app.
 Who Is It For? Our app is perfect for students, educators, and science enthusiasts of all ages. Whether you're a high school student exploring advanced physics, a teacher looking for engaging classroom resources, or simply curious about the wonders of the universe, our app offers a comprehensive and captivating learning experience. Download Now and Start Your Journey! Don't miss out on this opportunity to deepen your understanding of thermodynamics and physics. Download our app today and embark on a journey through the fascinating world of Maxwell's Demon and beyond. Discover how tiny particles drive big changes and unravel the mysteries of the universe with our cutting-edge educational tool! Available exclusively for iOS devices.

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‘The chainsaw never stops.’ Milei’s support survives his economic experiment.

Javier Milei, Argentina’s president, attends a G-7 meeting in Savelletri, Italy, on June 14, 2024. (Francesca Volpi/Bloomberg)

To say that life hasn’t been easy in Argentina since Javier Milei won the presidency would be to downplay the everyday reality of a nation undergoing the equivalent of economic surgery.

In the eight months since he took office, prices have soared more than 100%, consumer spending tanked and unemployment climbed as Argentines have been submitted to the most brutal austerity shock in recent history.

Yet something unexpected has happened on Milei’s watch: For all the ongoing misery, he remains just as popular as when he stormed to power pledging to take a chainsaw to the state. Even the hardest hit continue to swear by his bitter economic medicine.

Among them is Monica Perez, a 57-year-old butcher shop owner whose smile belies the fact the world’s onetime red-meat capital has seen beef consumption sink to the lowest in more than a century. Where construction workers who make up the bulk of her customers once ordered beef cuts by the kilogram, now they tell her how much money they have to spend and buy whatever that yields.

That’s indicative of a longer-term downward spiral as purchasing power plummeted under the previous left-wing government. It’s a trend that has accelerated under Milei. Perez, though, isn’t giving up on him yet.

“Of course I have hope,” she says at her shop in the neighborhood of La Union, an hour south of the city of Buenos Aires. “Things have to change. Things will change, for the better.”

Argentina is in the early stages of an economic and monetary experiment that will determine whether it can escape decades of decline and recapture some of its earlier swagger as a commodities superpower. Everywhere you look there are signs of decay, and the accompanying strains on its people.

More than half of Argentines now live below the poverty line as Milei’s “shock therapy” exacerbates the already staggering levels of destitution that he inherited.

Since assuming office in December, the libertarian president cut real pensions and public wages, halted nearly all public infrastructure projects, devalued the peso by more than 50% and did away with price controls on everything from milk to mobile phone bills.

As spending is slashed by the most in 30 years, homelessness is on the rise. Entire families have become mainstays outside supermarkets, begging for a bag of rice or pasta, and often ringing doorbells along the streets of the capital asking for used clothes.

Voters surveying the wreckage still accord the president a high degree of loyalty.

Milei’s popularity stands at a healthy 52%, a 1 percentage point bump from February, according to polling firm Management &amp; Fit. His immediate predecessor, Alberto Fernandez, racked up a disapproval rating of 79% by the end of his term, and is now fighting allegations of domestic abuse that risk compounding the now-opposition’s woes.

Cooling inflation - Milei’s central rallying cry - is one leg propping up his support. Monthly price increases fell from a three-decade high of 25.5% in December to 4% in July.

Residual anger at Peronism, the statist movement that’s governed Argentina for 16 of the last 20 years, most recently under Fernandez, helps explain the rest.

Perez, whose butcher shop sits on the corner of an unpaved road which has no access to a public sewage system, laments the decades of state largess with little to show for it beyond a litany of dismal statistics. “The majority of us are exhausted,” she said.

Monica Perez. (Anita Pouchard Serra/Bloomberg)

“That’s why we voted for him,” she added of Milei. “And why he won.”

Argentine leaders have long faced a tightrope walk between economic imperative and political expedience.

It’s a feat that traditionally involves a balance between fixing the economy’s many puzzles that require short-term pain while limiting the political costs and keeping the streets calm, according to Camila Perochena, a historian at Universidad Torcuato Di Tella in Buenos Aires. Milei threw that model out the window with a whatever-it-takes style that upended politics and shields his approval ratings, for now, from labor strikes and other habitual setbacks.

The result is “an unprecedented moment” under the country’s first economist president. Milei, she said, “has a conviction that he has to prioritize macroeconomic balance without considering the social cost or even the political costs that austerity measures will have.”

To be sure, Milei has tapped the brakes on shock therapy in recent months to keep inflation in check and protect the middle class, as he believes that forms the backbone of his administration, according to one of his top advisers, who asked not to be named discussing the president’s strategy. In July, Milei halted the removal of energy subsidies that had most households footing only 5% of the electricity cost; with inflation in check, the Economy Ministry restarted hiking prices in August.

Since a 54% devaluation in December, the government has rejected calls to speed up its 2% monthly depreciation of the official peso rate - or strip capital controls altogether - because they fear such a move would only juice prices more. To keep the parallel peso closer in line with the official rate, the government is intervening in the FX market, eating into the international reserves dutifully built up in the first months of austerity, rattling Wall Street in the process. Keeping the currency straitjacket in place only further postpones a recovery, most analysts estimate, fanning an already deep recession forecast to shrink the economy 3.7% this year.

So far, the self-styled anti-politician has proven more politically savvy than many anticipated. In June, Milei managed to muscle through the opposition-controlled congress a slew of economic reforms that remake labor laws, incentivize large foreign investments and even hike income taxes. He did so through relentless negotiations and cabinet changes, in spite of repeatedly referring to the legislative body as a rat’s nest. His more radical plans, like dollarizing the economy, sit on the back burner for now.

To untangle the edifice of capital controls put in place by his predecessors, revive activity and return to capital markets, Milei is pinning his hopes on a sizable loan from the International Monetary Fund - which Argentina already owes $44 billion. Yet the government’s currency intervention to keep inflation low flies in the face of the orthodox policy measures prescribed by the Washington-based lender, and the board needs convincing its biggest creditor deserves its 23rd chance. Milei still believes a new program could come as soon as this year.

Ultimately, it’s his ability to stabilize and reactivate the economy upon which he will be judged, according to Perochena, the historian.

Juan Pablo Rudoni is a case in point. His 300-employee modular construction company EcoSan suffered a 40% plunge in sales in the first half of the year, driven by Milei’s decision to cut public works spending that’s rippled across Argentina’s construction sector, one of the largest by employment. EcoSan boomed during the pandemic years by building modular hospitals and continues building housing or offices for industries like mining, oil and gas.

But Rudoni can’t deliver the last project Milei’s predecessors contracted: Two-story apartments and job-training offices destined for city slums. They’re practically finished, sitting idle in EcoSan’s cavernous factory outside Buenos Aires city. But Milei hasn’t even appointed an official to sign certificates that Rudoni needs to get paid and deliver. Meantime, his company’s utility bills have soared between 500% to 600% this year as Milei gradually withdraws subsidies that kept prices at absurdly low levels.

For all that, Rudoni backs Milei’s ambition to make Argentina a pro-business haven, and is willing to bite the bullet on utility bills. But he believes the austerity went too far, too fast. What’s more, Rudoni is opening a new factory later this year that he financed years ago, not anticipating the historic downturn.

He’s giving it until around the end of the year for the economy to pick up. Otherwise, he says, “it’ll be unsustainable for us to be able to keep our personnel and structure.”

“We need to see a light at the end of the tunnel,” added Rudoni. “But the issue is that light doesn’t seem within reach.”

Argentines didn’t turn to Milei blindly, of course. The country has spent more time in recession since the 1950s than any other nation, according to a World Bank report this year. An Argentine born when the country returned to democracy in 1983 has already lived through hyperinflation, record unemployment, sovereign defaults, multiple peso devaluations and several invented currencies that no longer exist. Much of that time has been spent in recession.

More recently, average incomes for white-collar payroll employees have plunged from $1,500 in 2017 to less than $500 last year, before ticking up on Milei’s watch, according to data compiled by Buenos Aires-based consulting firm EconViews.

The president acknowledges the pain and maintains that the “massive effort” being made by Argentines will pay off.

In any case, he’s not offering an alternative.

“Everything that can be cut, we will cut,” he said in a July 19 radio interview. “The chainsaw never stops.”

With assistance from Stephen Wicary.

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Awards Grant Greater Access to Nuclear Science User Facilities Resources and Capabilities

WASHINGTON, D.C.  — The U.S. Department of Energy (DOE) today awarded 13 Super Rapid Turnaround Experiment (RTE) user access projects to examine the performance of promising nuclear fuels and materials that support the nation’s current fleet of reactors and advance next-generation nuclear technologies.

Super RTE awards advance nuclear science and technology by providing researchers timely access to irradiation testing, post-irradiation examination, high-performance computing, and technical expertise.

Awardees will have up to a year to complete experiments using world-class testing capabilities provided through the Department’s Nuclear Science User Facilities (NSUF) program. These capabilities are equal to approximately $1.8 million in support and are provided at no cost to the researchers.

“Our national labs house unmatched nuclear science infrastructure and expertise,” said Acting Assistant Secretary for Nuclear Energy Dr. Michael Goff . “Access to these capabilities helps speed the research and development of materials and fuels needed to achieve the Biden-Harris Administration’s ambitious plans for the future of nuclear energy.”

The selected project teams include 13 principal investigators from 6 universities, 2 national laboratories, and 1 industry partner who will work with NSUF on the proposed experiments.

The projects will advance the development of nuclear fuels and materials such as silicon carbide composites, TRISO fuel, and advanced stainless steel. Projects will also research manufacturing and welding technologies for potential use in current light-water reactors and future advanced nuclear reactors. A complete list of the awarded projects can be viewed HERE .

The Super RTE solicitation is similar to NSUF’s traditional RTE solicitation, but it encourages larger scopes of work and allows researchers more time to perform tests. Awardees are also provided greater access to NSUF partner institutions, resources, and facilities, allowing for more complicated experiments.

The third and final round of traditional RTE awards for fiscal year 2024 will be announced later this fall. NSUF plans to issue another Super RTE solicitation in 2025.

NSUF advances DOE’s Office of Nuclear Energy’s mission through a consortium of state-of-the-art irradiation and post-irradiation testing facilities that can be utilized to support nuclear energy research and development. More information on NSUF and user access opportunities can be found  HERE .

NEWS MEDIA CONTACT:  Alexis Starks [email protected] (208) 526-4828

TechNet: Army CIO Details Exploration of LLMaaS in AI Experiments

U.S. Army Chief Information Officer (CIO) Leo Garciga said today that his team is working on a “wide swath of things” in the generative AI space and detailed the service branch’s exploration of large language models as a service (LLMaaS).

During the opening keynote of AFCEA International’s TechNet Augusta 2024 in Augusta, Ga., Garciga said “the idea of as a service in [the generative AI] space is going to be really, really important.”

The CIO highlighted that the Army is currently focused on “spending a lot of the time on the front end” by asking, “What problem are you trying to solve?” before deploying an LLM.

This process has resulted in the Army launching nine LLM efforts to lay the foundation for future use cases. Garciga offered examples of two of these programs, noting the service branch is actively leveraging generative AI for soldier wellbeing and aviation safety.

Garciga’s office released its first version of generative AI guidance for the Army in late June and said they have been partnering with small businesses in this space to get a “good lay of the land.”

“I’m really focused on large language models right now,” Garciga said. “What we’re really finding, and our observations have been: conversational AI … That’s where we’re seeing a lot of folks work through [for back-office stuff].”

“The Army has a lot of tasks,” Garciga said. “How do we better search, go through those tasks … It would be nice if we could just get some lift to get there faster and reduce that cognitive burden. [There’s] lot of work in that space – a lot of work in the mission space, on the cyber side, in this area, too – but we’re seeing mostly back-office.”

“The AI assistant piece has been really, really interesting. This idea of much smaller models and integrating them together, [we’re] spending a lot of time in that space right now. One, to get a good lay of the land. Where do these things make sense? And also, to think through this idea of, are there specific Army models we have to worry about,” Garciga said. “We’ve got our own culture. We’ve got our own way of doing business. How do we shape that?”

The CIO highlighted that the number one use case he sees right now for LLMs is through the Army Judge Advocate General’s Corps. “Attorneys are really good at using this stuff, and it has cut down our time to get policy out and to move through the bureaucracy of the department because they’ve been able to use LLMs to triage law.”

“I’m super excited in this space right now, because really, industry has come to the table in a very good way with lots of lessons learned. Everybody’s been pragmatic, [saying], ‘Here’s where we are in the story. This isn’t a 100 percent solution, maybe this gives you some lift,’” he continued, adding, “I think this next stage is, how do we make it available more broadly?”

Army Set to Release cATO Guidance, CIO Previews new Pilots

Separately during his opening keynote this morning, the CIO highlighted that he is about two weeks out from signing new guidance that would give the Army fresh direction on achieving continuous authorization for DevSecOps platforms.

The Pentagon released its own continuous authorization guidance in April, which seeks to guide defense agencies to achieve continuous authorization (cATO) to operate DevSecOps platforms and other applications produced by a software factory as part of efforts to counter cyber threats.

For defense agencies to deliver new features rapidly, they need an authorization process that keeps pace with continuous change for a developing capability – a cATO. Many defense agencies have identified obtaining an “authorization to operate” as the longest step in developing and deploying software.

Garciga said today during TechNet Augusta 2024 that the Army’s cATO guidance document is “in the final stages.”

“It’s inside the Army’s bureaucracy,” he said. “We’re about two weeks out from it being somewhere near my desk for signing.”

Garciga said the forthcoming guidance is “really simple.”

“Have an initial ATO, go as fast as you can and deliver software. Don’t need to redo [the ATO]. Don’t need to have a secondary ATO. This is really about, can we deliver directly to production,” the CIO said.

Garciga said his team is “comfortable with what we’re seeing right now” in terms of the two cATO pilot programs the Army has deployed.

“We’ve got two programs: PEO Soldier – PM Nett Warrior; we’ve been working with them over the last six months. [I’m] very comfortable with where they are in the story. They will probably be the first, no joke, real Army cATO at scale, with multiple tenants,” Garciga said. “The other one’s going to be combination with ARCYBER and PM-DCO – Gabriel Nimbus.”

“Big shout out to ARCYBER for really working this piece hard over the last two and a half, three years. I think that’s really important – we didn’t wake up last year and say we were going to do this. This has been, in many ways, about a three-year effort between some folks in the Army G2, some folks in CIO, some folks in G6, and some folks at ARCYBER to really lay the foundation to make this happen for the Army.”

The CIO said the cATO guidance document for the Army should be published in the next 30 days.

“[We’re] definitely looking for feedback,” he told the audience of more than 6,000 attendees. “[It’s] probably an 80 percent solution. I’m going to admit it, we probably don’t have 100 percent. [There are] still some challenges on how does this look for hardware in the loop … So I think we need some help on rethinking what that looks like.”

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