real life experiment

The Trolley Problem Has Been Tested in 'Real Life' For The Very First Time

It's called the trolley problem, and it's all about how far you'd be willing to go to save lives in an emergency – even if it meant killing somebody.

Now, scientists have tested this famous thought experiment in real life for the first time: with almost 200 human participants, caged mice, electric shocks – and one heck of a decision to make.

You're probably already aware of the classic trolley problem itself, but here's a quick recap - because it's essential to be familiar with it to understand the moral dilemma posed in the new experiments.

Imagine seeing a runaway trolley (or train carriage) hurtling down the tracks, headed directly for five people who are tied to the rails ahead.

The good news is you have the power to save their lives – by simply pulling a lever that will divert the runaway trolley onto another track so that it avoids these poor, tied-up people.

There's just one problem, but it's a big one.

On the other track, there's also a single person tied to the rails, and if you intervene to save the five people on the original track, you'll end up killing this other person.

There are lots of variants and twists that expand upon the dilemma of this classic scenario, each giving a different spin on the hypothetical rightness and wrongness of pulling the metaphorical lever (or not).

But at its heart, the ethical question posed by the trolley problem is whether you should save five lives by taking one – which means getting your hands dirty – or if you should refrain from actively choosing to kill someone, which perversely results in even more death.

This probing dilemma has pondered moral philosophers since the 1960s, but in a provocative twist on the classic problem, psychologists in Belgium have brought the nightmare scenario into the real world (or at least half-way, you might say).

In an experiment with almost 200 student volunteers, participants were admitted to a lab, one at a time, and presented with a difficult choice.

In the lab, an electroshock machine was connected to two separate cages. One of these cages had five mice within it. The other cage had a single mouse occupant. You can probably tell where this is going.

The participants were told they had 20 seconds to make a decision. If they did nothing, a very painful but non-lethal electric shock would be applied to the cage containing the five mice.

If, however, they simply pressed a single button placed before them, then those five mice would be spared the electric shock, which would instead be administered to the single mouse in the other cage.

Before you start penning hate mail, please note: in actual fact, no animals were ever shocked or otherwise harmed in the test.

But during the experiment this was never explained to the participants, who were given the impression their decision would result in electric shocks being applied to at least one mouse, or at most five, depending on how they chose to react.

Ultimately, 84 percent of the participants who took part in the real-life test elected to press the button, sparing the five mice by consciously choosing to zap the other mouse – which, you might reason, results in fewer animals suffering overall ( if they were receiving shocks, which they weren't).

What's interesting is that this real-life experiment didn't match up with another experiment run by the researchers, in which they asked a separate group of participants how they would react in the exact same situation. This time it was purely hypothetical, with no lab setup, mice, or electroshock machine actually present.

In that experiment, only 66 percent of people said they would zap the solitary mouse.

There are a number of limitations with the study, and the extent to which it fully embodies the trolley problem.

For a start, it's hard to ethically equate the prospect of human death with the experience of a mouse receiving an electric shock, and at least some of the participants involved in the experiment later said they saw through the researchers' setup, understanding no animals would be harmed.

But to the extent that it explores the trolley problem, the results suggest that, in the heat of the moment, more of us lean towards consequentialism (based on the overall outcome) than deontological thought (which argues it would be immoral to act to hurt the one mouse, despite the overall outcome), than we might otherwise think.

Hmm. Lots of tricky questions, and no clear answers. What do you think you would do?

The findings are reported in Psychological Science .

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We can all agree that science is awesome. And you can bring that awesomeness into your very own home with these 20 safe DIY experiments you can do right now with ordinary household items.

1. Make Objects Seemingly Disappear Refraction is when light changes direction and speed as it passes from one object to another. Only visible objects reflect light. When two materials with similar reflective properties come into contact, light will pass through both materials at the same speed, rendering the other material invisible. Check out this video from BritLab  on how to turn glass invisible using vegetable oil and pyrex glass.

2. Freeze Water Instantly When purified water is cooled to just below freezing point, a quick nudge or an icecube placed in it is all it takes for the water to instantly freeze. You can finally have the power of Frozone from The Incredibles on a very small scale! Check out the video on this "cool" experiment. 

3. Create Oobleck And Make It Dance To The Music Named after a sticky substance in a children’s book by Dr Seuss , Oobleck is a non-Newtonian fluid, which means it can behave as both a solid and a liquid. And when placed on a sound source, the vibrations causes the mixture to gloopily dance. Check out these instructions from Housing A Forest  on how to make this groovy fluid funk out in every way.

4. Create Your Own Hybrid Rocket Engine With a combination of a solid fuel source and a liquid oxidizer, hybrid rocket engines can propel themselves. And on a small scale, you can create your own hybrid rocket engine, using pasta, mouthwash and yeast. Sadly, it won’t propel much, but who said rocket science ain’t easy? Check out this video from NightHawkInLight on how to make this mini engine.

5. Create "Magic Mud" Another non-Newtonian fluid here, this time from the humble potato. "Magic Mud" is actually starch found in potatoes. It’ll remain hard when handled but leave it alone and it turns into a liquid. Make your own “Magic Mud” with this video.

6. Command The Skies And Create A Cloud In A Bottle Not quite a storm in a teacup, but it is a cloud in a bottle. Clouds up in the sky are formed when water vapor cools and condenses into visible water droplets. Create your own cloud in a bottle using a few household items with these wikiHow instructions .

7. Create An Underwater Magical World First synthesized by Adolf van Baeyer in 1871, fluorescein is a non-toxic powder found in highlighter pens, and used by NASA to find shuttles that land in the sea. Create an underwater magical world with this video from NightHawkInLight .

9. Make Your Own Lava Lamp Inside a lava lamp are colored bubbles of wax suspended in a clear or colorless liquid, which changes density when warmed by a heating element at the base, allowing them to rise and fall hypnotically. Create your own lava lamp with these video instructions.

10. Create Magnetic Fluid A ferrofluid is a liquid that contains nanoscale particles of metal, which can become magnetized. And with oil, toner and a magnet , you can create your own ferrofluid and harness the power of magnetism! 

12. Make Waterproof Sand A hydrophobic substance is one that repels water. When sand is combined with a water-resistant chemical, it becomes hydrophobic. So when it comes into contact with water, the sand will remain dry and reusable. Make your own waterproof sand with this video .

13. Make Elephant's Toothpaste Elephant’s toothpaste is a steaming foamy substance created by the rapid decomposition of hydrogen peroxide, which sort of resembles giant-sized toothpaste. Make your own elephant’s toothpaste with these instructions.

14. Make Crystal Bubbles When the temperature falls below 0 o C (32 o F), it’s possible to freeze bubbles into crystals. No instructions needed here, just some bubble mix and chilly weather.

15. Make Moving Liquid Art Mixing dish soap and milk together causes the surface tension of the milk to break down. Throw in different food colorings and create this trippy chemical reaction.

16. Create Colourful Carnations Flowers absorb water through their stems, and if that water has food coloring in it, the flowers will also absorb that color. Create some wonderfully colored flowers with these wikiHow instructions .

17. "Magically" Turn Water Into Wine Turn water into wine with this  video  by experimenter Dave Hax . Because water has a higher density than wine, they can switch places. Amaze your friends with this fun science trick.

18. Release The Energy In Candy (Without Eating It) Dropping a gummy bear into a test tube with potassium chlorate releases the chemical energy inside in an intense chemical reaction. That’s exactly what's happening when you eat candy, kids.

19. Make Water "Mysteriously" Disappear Sodium polyacrylate is a super-absorbent polymer, capable of absorbing up to 300 times its own weight in water. Found in disposable diapers, you can make water disappear in seconds with this video .

20. Create A Rainbow In A Jar Different liquids have different masses and different densities. For example, oil is less dense than water and will float on top of its surface. By combining liquids of different densities and adding food coloring, you can make an entire rainbow in a jar with this video .

There you have it – 20 experiments for you to explore the incredible world of science!

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The Top 10 Science Experiments of All Time

These seminal experiments changed our understanding of the universe and ourselves..

Every day, we conduct science experiments, posing an “if” with a “then” and seeing what shakes out. Maybe it’s just taking a slightly different route on our commute home or heating that burrito for a few seconds longer in the microwave. Or it could be trying one more variation of that gene, or wondering what kind of code would best fit a given problem. Ultimately, this striving, questioning spirit is at the root of our ability to discover anything at all. A willingness to experiment has helped us delve deeper into the nature of reality through the pursuit we call science. 

A select batch of these science experiments has stood the test of time in showcasing our species at its inquiring, intelligent best. Whether elegant or crude, and often with a touch of serendipity, these singular efforts have delivered insights that changed our view of ourselves or the universe. 

Here are nine such successful endeavors — plus a glorious failure — that could be hailed as the top science experiments of all time.

Eratosthenes Measures the World

Experimental result: The first recorded measurement of Earth’s circumference 

When: end of the third century B.C.

Just how big is our world? Of the many answers from ancient cultures, a stunningly accurate value calculated by Eratosthenes has echoed down the ages. Born around 276 B.C. in Cyrene, a Greek settlement on the coast of modern-day Libya, Eratosthenes became a voracious scholar — a trait that brought him both critics and admirers. The haters nicknamed him Beta, after the second letter of the Greek alphabet. University of Puget Sound physics professor James Evans explains the Classical-style burn: “Eratosthenes moved so often from one field to another that his contemporaries thought of him as only second-best in each of them.” Those who instead celebrated the multitalented Eratosthenes dubbed him Pentathlos, after the five-event athletic competition.

That mental dexterity landed the scholar a gig as chief librarian at the famous library in Alexandria, Egypt. It was there that he conducted his famous experiment. He had heard of a well in Syene, a Nile River city to the south (modern-day Aswan), where the noon sun shone straight down, casting no shadows, on the date of the Northern Hemisphere’s summer solstice. Intrigued, Eratosthenes measured the shadow cast by a vertical stick in Alexandria on this same day and time. He determined the angle of the sun’s light there to be 7.2 degrees, or 1/50th of a circle’s 360 degrees. 

Knowing — as many educated Greeks did — Earth was spherical, Eratosthenes fathomed that if he knew the distance between the two cities, he could multiply that figure by 50 and gauge Earth’s curvature, and hence its total circumference. Supplied with that information, Eratosthenes deduced Earth’s circumference as 250,000 stades, a Hellenistic unit of length equaling roughly 600 feet. The span equates to about 28,500 miles, well within the ballpark of the correct figure of 24,900 miles. 

Eratosthenes’ motive for getting Earth’s size right was his keenness for geography, a field whose name he coined. Fittingly, modernity has bestowed upon him one more nickname: father of geography. Not bad for a guy once dismissed as second-rate.

William Harvey Takes the Pulse of Nature

Experimental result: The discovery of blood circulation

When: Theory published in 1628

Boy, was Galen wrong. 

The Greek physician-cum-philosopher proposed a model of blood flow in the second century that, despite being full of whoppers, prevailed for nearly 1,500 years. Among its claims: The liver constantly makes new blood from food we eat; blood flows throughout the body in two separate streams, one infused (via the lungs) with “vital spirits” from air; and the blood that tissues soak up never returns to the heart. 

Overturning all this dogma took a series of often gruesome experiments. 

High-born in England in 1578, William Harvey rose to become royal physician to King James I, affording him the time and means to pursue his greatest interest: anatomy. He first hacked away (literally, in some cases) at the Galenic model by exsanguinating — draining the blood from — test critters, including sheep and pigs. Harvey realized that if Galen were right, an impossible volume of blood, exceeding the animals’ size, would have to pump through the heart every hour. 

To drive this point home, Harvey sliced open live animals in public, demonstrating their puny blood supplies. He also constricted blood flow into a snake’s exposed heart by finger-pinching a main vein. The heart shrunk and paled; when pierced, it poured forth little blood. By contrast, choking off the main exiting artery swelled the heart. Through studies of the slow heart beats of reptiles and animals near death, he discerned the heart’s contractions, and deduced that it pumped blood through the body in a circuit.

According to Andrew Gregory, a professor of history and philosophy of science at University College London, this was no easy deduction on Harvey’s part. “If you look at a heart beating normally in its normal surroundings, it is very difficult to work out what is actually happening,” he says. 

Experiments with willing people, which involved temporarily blocking blood flow in and out of limbs, further bore out Harvey’s revolutionary conception of blood circulation. He published the full theory in a 1628 book, De Motu Cordis [The Motion of the Heart]. His evidence-based approach transformed medical science, and he’s recognized today as the father of modern medicine and physiology.

Gregor Mendel Cultivates Genetics

Experimental result: The fundamental rules of genetic inheritance 

When: 1855-1863 

A child, to varying degrees, resembles a parent, whether it’s a passing resemblance or a full-blown mini-me. Why? 

The profound mystery behind the inheritance of physical traits began to unravel a century and a half ago, thanks to Gregor Mendel. Born in 1822 in what is now the Czech Republic, Mendel showed a knack for the physical sciences, though his farming family had little money for formal education. Following the advice of a professor, he joined the Augustinian order, a monastic group that emphasized research and learning, in 1843. 

Ensconced at a monastery in Brno, the shy Gregor quickly began spending time in the garden. Fuchsias in particular grabbed his attention, their daintiness hinting at an underlying grand design. “The fuchsias probably gave him the idea for the famous experiments,” says Sander Gliboff, who researches the history of biology at Indiana University Bloomington. “He had been crossing different varieties, trying to get new colors or combinations of colors, and he got repeatable results that suggested some law of heredity at work.”

These laws became clear with his cultivation of pea plants. Using paintbrushes, Mendel dabbed pollen from one to another, precisely pairing thousands of plants with certain traits over a stretch of about seven years. He meticulously documented how matching yellow peas and green peas, for instance, always yielded a yellow plant. Yet mating these yellow offspring together produced a generation where a quarter of the peas gleamed green again. Ratios like these led to Mendel’s coining of the terms dominant (the yellow color, in this case) and recessive for what we now call genes, and which Mendel referred to as “factors.” 

He was ahead of his time. His studies received scant attention in their day, but decades later, when other scientists discovered and replicated Mendel’s experiments, they came to be regarded as a breakthrough. 

“The genius in Mendel’s experiments was his way of formulating simple hypotheses that explain a few things very well, instead of tackling all the complexities of heredity at once,” says Gliboff. “His brilliance was in putting it all together into a project that he could actually do.”

Isaac Newton Eyes Optics

Experimental result: The nature of color and light

When: 1665-1666

Before he was that Isaac Newton — scientist extraordinaire and inventor of the laws of motion, calculus and universal gravitation (plus a crimefighter to boot) — plain ol’ Isaac found himself with time to kill. To escape a devastating outbreak of plague in his college town of Cambridge, Newton holed up at his boyhood home in the English countryside. There, he tinkered with a prism he picked up at a local fair — a “child’s plaything,” according to Patricia Fara, fellow of Clare College, Cambridge. 

Let sunlight pass through a prism and a rainbow, or spectrum, of colors splays out. In Newton’s time, prevailing thinking held that light takes on the color from the medium it transits, like sunlight through stained glass. Unconvinced, Newton set up a prism experiment that proved color is instead an inherent property of light itself. This revolutionary insight established the field of optics, fundamental to modern science and technology. 

Newton deftly executed the delicate experiment: He bored a hole in a window shutter, allowing a single beam of sunlight to pass through two prisms. By blocking some of the resulting colors from reaching the second prism, Newton showed that different colors refracted, or bent, differently through a prism. He then singled out a color from the first prism and passed it alone through the second prism; when the color came out unchanged, it proved the prism didn’t affect the color of the ray. The medium did not matter. Color was tied up, somehow, with light itself. 

Partly owing to the ad hoc, homemade nature of Newton’s experimental setup, plus his incomplete descriptions in a seminal 1672 paper, his contemporaries initially struggled to replicate the results. “It’s a really, really technically difficult experiment to carry out,” says Fara. “But once you have seen it, it’s incredibly convincing.” 

In making his name, Newton certainly displayed a flair for experimentation, occasionally delving into the self-as-subject variety. One time, he stared at the sun so long he nearly went blind. Another, he wormed a long, thick needle under his eyelid, pressing on the back of his eyeball to gauge how it affected his vision. Although he had plenty of misses in his career — forays into occultism, dabbling in biblical numerology — Newton’s hits ensured his lasting fame.

Michelson and Morley Whiff on Ether

Experimental result: The way light moves

Say “hey!” and the sound waves travel through a medium (air) to reach your listener’s ears. Ocean waves, too, move through their own medium: water. Light waves are a special case, however. In a vacuum, with all media such as air and water removed, light somehow still gets from here to there. How can that be? 

The answer, according to the physics en vogue in the late 19th century, was an invisible, ubiquitous medium delightfully dubbed the “luminiferous ether.” Working together at what is now Case Western Reserve University in Ohio, Albert Michelson and Edward W. Morley set out to prove this ether’s existence. What followed is arguably the most famous failed experiment in history. 

The scientists’ hypothesis was thus: As Earth orbits the sun, it constantly plows through ether, generating an ether wind. When the path of a light beam travels in the same direction as the wind, the light should move a bit faster compared with sailing against the wind. 

To measure the effect, miniscule though it would have to be, Michelson had just the thing. In the early 1880s, he had invented a type of interferometer, an instrument that brings sources of light together to create an interference pattern, like when ripples on a pond intermingle. A Michelson interferometer beams light through a one-way mirror. The light splits in two, and the resulting beams travel at right angles to each other. After some distance, they reflect off mirrors back toward a central meeting point. If the light beams arrive at different times, due to some sort of unequal displacement during their journeys (say, from the ether wind), they create a distinctive interference pattern. 

The researchers protected their delicate interferometer setup from vibrations by placing it atop a solid sandstone slab, floating almost friction-free in a trough of mercury and further isolated in a campus building’s basement. Michelson and Morley slowly rotated the slab, expecting to see interference patterns as the light beams synced in and out with the ether’s direction. 

Instead, nothing. Light’s speed did not vary. 

Neither researcher fully grasped the significance of their null result. Chalking it up to experimental error, they moved on to other projects. (Fruitfully so: In 1907, Michelson became the first American to win a Nobel Prize, for optical instrument-based investigations.) But the huge dent Michelson and Morley unintentionally kicked into ether theory set off a chain of further experimentation and theorizing that led to Albert Einstein’s 1905 breakthrough new paradigm of light, special relativity.

Marie Curie’s Work Matters

Experimental result: Defining radioactivity 

Few women are represented in the annals of legendary scientific experiments, reflecting their historical exclusion from the discipline. Marie Sklodowska broke this mold. 

Born in 1867 in Warsaw, she immigrated to Paris at age 24 for the chance to further study math and physics. There, she met and married physicist Pierre Curie, a close intellectual partner who helped her revolutionary ideas gain a foothold within the male-dominated field. “If it wasn’t for Pierre, Marie would never have been accepted by the scientific community,” says Marilyn B. Ogilvie, professor emeritus in the history of science at the University of Oklahoma. “Nonetheless, the basic hypotheses — those that guided the future course of investigation into the nature of radioactivity — were hers.”

The Curies worked together mostly out of a converted shed on the college campus where Pierre worked. For her doctoral thesis in 1897, Marie began investigating a newfangled kind of radiation, similar to X-rays and discovered just a year earlier. Using an instrument called an electrometer, built by Pierre and his brother, Marie measured the mysterious rays emitted by thorium and uranium. Regardless of the elements’ mineralogical makeup — a yellow crystal or a black powder, in uranium’s case — radiation rates depended solely on the amount of the element present. 

From this observation, Marie deduced that the emission of radiation had nothing to do with a substance’s molecular arrangements. Instead, radioactivity — a term she coined — was an inherent property of individual atoms, emanating from their internal structure. Up until this point, scientists had thought atoms elementary, indivisible entities. Marie had cracked the door open to understanding matter at a more fundamental, subatomic level. 

Curie was the first woman to win a Nobel Prize, in 1903, and one of a very select few people to earn a second Nobel, in 1911 (for her later discoveries of the elements radium and polonium). 

“In her life and work,” says Ogilvie, “she became a role model for young women who wanted a career in science.”

Ivan Pavlov Salivates at the Idea

Experimental result: The discovery of conditioned reflexes

When: 1890s-1900s

Russian physiologist Ivan Pavlov scooped up a Nobel Prize in 1904 for his work with dogs, investigating how saliva and stomach juices digest food. While his scientific legacy will always be tied to doggie drool, it is the operations of the mind — canine, human and otherwise — for which Pavlov remains celebrated today.

Gauging gastric secretions was no picnic. Pavlov and his students collected the fluids that canine digestive organs produced, with a tube suspended from some pooches’ mouths to capture saliva. Come feeding time, the researchers began noticing that dogs who were experienced in the trials would start drooling into the tubes before they’d even tasted a morsel. Like numerous other bodily functions, the generation of saliva was considered a reflex at the time, an unconscious action only occurring in the presence of food. But Pavlov’s dogs had learned to associate the appearance of an experimenter with meals, meaning the canines’ experience had conditioned their physical responses. 

“Up until Pavlov’s work, reflexes were considered fixed or hardwired and not changeable,” says Catharine Rankin, a psychology professor at the University of British Columbia and president of the Pavlovian Society. “His work showed that they could change as a result of experience.” 

Pavlov and his team then taught the dogs to associate food with neutral stimuli as varied as buzzers, metronomes, rotating objects, black squares, whistles, lamp flashes and electric shocks. Pavlov never did ring a bell, however; credit an early mistranslation of the Russian word for buzzer for that enduring myth. 

The findings formed the basis for the concept of classical, or Pavlovian, conditioning. It extends to essentially any learning about stimuli, even if reflexive responses are not involved. “Pavlovian conditioning is happening to us all of the time,” says W. Jeffrey Wilson of Albion College, fellow officer of the Pavlovian Society. “Our brains are constantly connecting things we experience together.” In fact, trying to “un-wire” these conditioned responses is the strategy behind modern treatments for post-traumatic stress disorder, as well as addiction.

Robert Millikan Gets a Charge

Experimental result: The precise value of a single electron’s charge

By most measures, Robert Millikan had done well for himself. Born in 1868 in a small town in Illinois, he went on to earn degrees from Oberlin College and Columbia University. He studied physics with European luminaries in Germany. He then joined the University of Chicago’s physics department, and even penned some successful textbooks. 

But his colleagues were doing far more. The turn of the 20th century was a heady time for physics: In the span of just over a decade, the world was introduced to quantum physics, special relativity and the electron — the first evidence that atoms had divisible parts. By 1908, Millikan found himself pushing 40 without a significant discovery to his name. 

The electron, though, offered an opportunity. Researchers had struggled with whether the particle represented a fundamental unit of electric charge, the same in all cases. It was a critical determination for further developing particle physics. With nothing to lose, Millikan gave it a go. 

In his lab at the University of Chicago, he began working with containers of thick water vapor, called cloud chambers, and varying the strength of an electric field within them. Clouds of water droplets formed around charged atoms and molecules before descending due to gravity. By adjusting the strength of the electric field, he could slow down or even halt a single droplet’s fall, countering gravity with electricity. Find the precise strength where they balanced, and — assuming it did so consistently — that would reveal the charge’s value. 

When it turned out water evaporated too quickly, Millikan and his students — the often-unsung heroes of science — switched to a longer-lasting substance: oil, sprayed into the chamber by a drugstore perfume atomizer. 

The increasingly sophisticated oil-drop experiments eventually determined that the electron did indeed represent a unit of charge. They estimated its value to within whiskers of the currently accepted charge of one electron (1.602 x 10-19 coulombs). It was a coup for particle physics, as well as Millikan. 

“There’s no question that it was a brilliant experiment,” says Caltech physicist David Goodstein. “Millikan’s result proved beyond reasonable doubt that the electron existed and was quantized with a definite charge. All of the discoveries of particle physics follow from that.”

Young, Davisson and Germer See Particles Do the Wave

Experimental result: The wavelike nature of light and electrons 

When: 1801 and 1927, respectively 

Light: particle or wave? Having long wrestled with this seeming either/or, many physicists settled on particle after Isaac Newton’s tour de force through optics. But a rudimentary, yet powerful, demonstration by fellow Englishman Thomas Young shattered this convention. 

Young’s interests covered everything from Egyptology (he helped decode the Rosetta Stone) to medicine and optics. To probe light’s essence, Young devised an experiment in 1801. He cut two thin slits into an opaque object, let sunlight stream through them and watched how the beams cast a series of bright and dark fringes on a screen beyond. Young reasoned that this pattern emerged from light wavily spreading outward, like ripples across a pond, with crests and troughs from different light waves amplifying and canceling each other. 

Although contemporary physicists initially rebuffed Young’s findings, rampant rerunning of these so-called double-slit experiments established that the particles of light really do move like waves. “Double-slit experiments have become so compelling [because] they are relatively easy to conduct,” says David Kaiser, a professor of physics and of the history of science at MIT. “There is an unusually large ratio, in this case, between the relative simplicity and accessibility of the experimental design and the deep conceptual significance of the results.”

More than a century later, a related experiment by Clinton Davisson and Lester Germer showed the depth of this significance. At what is now called Nokia Bell Labs in New Jersey, the physicists ricocheted electron particles off a nickel crystal. The scattered electrons interacted to produce a pattern only possible if the particles also acted like waves. Subsequent double slit-style experiments with electrons proved that particles with matter and undulating energy (light) can each act like both particles and waves. The paradoxical idea lies at the heart of quantum physics, which at the time was just beginning to explain the behavior of matter at a fundamental level. 

“What these experiments show, at their root, is that the stuff of the world, be it radiation or seemingly solid matter, has some irreducible, unavoidable wavelike characteristics,” says Kaiser. “No matter how surprising or counterintuitive that may seem, physicists must take that essential ‘waviness’ into account.”

Robert Paine Stresses Starfish

Experimental result: The disproportionate impact of keystone species on ecosystems

When: Initially presented in a 1966 paper

Just like the purple starfish he crowbarred off rocks and chucked into the Pacific Ocean, Bob Paine threw conventional wisdom right out the window. 

By the 1960s, ecologists had come to agree that habitats thrived primarily through diversity. The common practice of observing these interacting webs of creatures great and small suggested as much. Paine took a different approach. 

Curious what would happen if he intervened in an environment, Paine ran his starfish-banishing experiments in tidal pools along and off the rugged coast of Washington state. The removal of this single species, it turned out, could destabilize a whole ecosystem. Unchecked, the starfish’s barnacle prey went wild — only to then be devoured by marauding mussels. These shellfish, in turn, started crowding out the limpets and algal species. The eventual result: a food web in tatters, with only mussel-dominated pools left behind. 

Paine dubbed the starfish a keystone species, after the necessary center stone that locks an arch into place. A revelatory concept, it meant that all species do not contribute equally in a given ecosystem. Paine’s discovery had a major influence on conservation, overturning the practice of narrowly preserving an individual species for the sake of it, versus an ecosystem-based management strategy.

“His influence was absolutely transformative,” says Oregon State University’s Jane Lubchenco, a marine ecologist. She and her husband, fellow OSU professor Bruce Menge, met 50 years ago as graduate students in Paine’s lab at the University of Washington. Lubchenco, the administrator of the National Oceanic Atmospheric Administration from 2009 to 2013, saw over the years the impact that Paine’s keystone species concept had on policies related to fisheries management.

Lubchenco and Menge credit Paine’s inquisitiveness and dogged personality for changing their field. “A thing that made him so charismatic was almost a childlike enthusiasm for ideas,” says Menge. “Curiosity drove him to start the experiment, and then he got these spectacular results.”

Paine died in 2016. His later work had begun exploring the profound implications of humans as a hyper-keystone species, altering the global ecosystem through climate change and unchecked predation.

Adam Hadhazy is based in New Jersey. His work has also appeared in New Scientist and Popular Science , among other publications. This story originally appeared in print as "10 Experiments That Changed Everything"

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19+ Experimental Design Examples (Methods + Types)

Ever wondered how scientists discover new medicines, psychologists learn about behavior, or even how marketers figure out what kind of ads you like? Well, they all have something in common: they use a special plan or recipe called an "experimental design."

Imagine you're baking cookies. You can't just throw random amounts of flour, sugar, and chocolate chips into a bowl and hope for the best. You follow a recipe, right? Scientists and researchers do something similar. They follow a "recipe" called an experimental design to make sure their experiments are set up in a way that the answers they find are meaningful and reliable.

Experimental design is the roadmap researchers use to answer questions. It's a set of rules and steps that researchers follow to collect information, or "data," in a way that is fair, accurate, and makes sense.

Long ago, people didn't have detailed game plans for experiments. They often just tried things out and saw what happened. But over time, people got smarter about this. They started creating structured plans—what we now call experimental designs—to get clearer, more trustworthy answers to their questions.

In this article, we'll take you on a journey through the world of experimental designs. We'll talk about the different types, or "flavors," of experimental designs, where they're used, and even give you a peek into how they came to be.

What Is Experimental Design?

Alright, before we dive into the different types of experimental designs, let's get crystal clear on what experimental design actually is.

Imagine you're a detective trying to solve a mystery. You need clues, right? Well, in the world of research, experimental design is like the roadmap that helps you find those clues. It's like the game plan in sports or the blueprint when you're building a house. Just like you wouldn't start building without a good blueprint, researchers won't start their studies without a strong experimental design.

So, why do we need experimental design? Think about baking a cake. If you toss ingredients into a bowl without measuring, you'll end up with a mess instead of a tasty dessert.

Similarly, in research, if you don't have a solid plan, you might get confusing or incorrect results. A good experimental design helps you ask the right questions ( think critically ), decide what to measure ( come up with an idea ), and figure out how to measure it (test it). It also helps you consider things that might mess up your results, like outside influences you hadn't thought of.

For example, let's say you want to find out if listening to music helps people focus better. Your experimental design would help you decide things like: Who are you going to test? What kind of music will you use? How will you measure focus? And, importantly, how will you make sure that it's really the music affecting focus and not something else, like the time of day or whether someone had a good breakfast?

In short, experimental design is the master plan that guides researchers through the process of collecting data, so they can answer questions in the most reliable way possible. It's like the GPS for the journey of discovery!

History of Experimental Design

Around 350 BCE, people like Aristotle were trying to figure out how the world works, but they mostly just thought really hard about things. They didn't test their ideas much. So while they were super smart, their methods weren't always the best for finding out the truth.

Fast forward to the Renaissance (14th to 17th centuries), a time of big changes and lots of curiosity. People like Galileo started to experiment by actually doing tests, like rolling balls down inclined planes to study motion. Galileo's work was cool because he combined thinking with doing. He'd have an idea, test it, look at the results, and then think some more. This approach was a lot more reliable than just sitting around and thinking.

Now, let's zoom ahead to the 18th and 19th centuries. This is when people like Francis Galton, an English polymath, started to get really systematic about experimentation. Galton was obsessed with measuring things. Seriously, he even tried to measure how good-looking people were ! His work helped create the foundations for a more organized approach to experiments.

Next stop: the early 20th century. Enter Ronald A. Fisher , a brilliant British statistician. Fisher was a game-changer. He came up with ideas that are like the bread and butter of modern experimental design.

Fisher invented the concept of the " control group "—that's a group of people or things that don't get the treatment you're testing, so you can compare them to those who do. He also stressed the importance of " randomization ," which means assigning people or things to different groups by chance, like drawing names out of a hat. This makes sure the experiment is fair and the results are trustworthy.

Around the same time, American psychologists like John B. Watson and B.F. Skinner were developing " behaviorism ." They focused on studying things that they could directly observe and measure, like actions and reactions.

Skinner even built boxes—called Skinner Boxes —to test how animals like pigeons and rats learn. Their work helped shape how psychologists design experiments today. Watson performed a very controversial experiment called The Little Albert experiment that helped describe behaviour through conditioning—in other words, how people learn to behave the way they do.

In the later part of the 20th century and into our time, computers have totally shaken things up. Researchers now use super powerful software to help design their experiments and crunch the numbers.

With computers, they can simulate complex experiments before they even start, which helps them predict what might happen. This is especially helpful in fields like medicine, where getting things right can be a matter of life and death.

Also, did you know that experimental designs aren't just for scientists in labs? They're used by people in all sorts of jobs, like marketing, education, and even video game design! Yes, someone probably ran an experiment to figure out what makes a game super fun to play.

So there you have it—a quick tour through the history of experimental design, from Aristotle's deep thoughts to Fisher's groundbreaking ideas, and all the way to today's computer-powered research. These designs are the recipes that help people from all walks of life find answers to their big questions.

Key Terms in Experimental Design

Before we dig into the different types of experimental designs, let's get comfy with some key terms. Understanding these terms will make it easier for us to explore the various types of experimental designs that researchers use to answer their big questions.

Independent Variable : This is what you change or control in your experiment to see what effect it has. Think of it as the "cause" in a cause-and-effect relationship. For example, if you're studying whether different types of music help people focus, the kind of music is the independent variable.

Dependent Variable : This is what you're measuring to see the effect of your independent variable. In our music and focus experiment, how well people focus is the dependent variable—it's what "depends" on the kind of music played.

Control Group : This is a group of people who don't get the special treatment or change you're testing. They help you see what happens when the independent variable is not applied. If you're testing whether a new medicine works, the control group would take a fake pill, called a placebo , instead of the real medicine.

Experimental Group : This is the group that gets the special treatment or change you're interested in. Going back to our medicine example, this group would get the actual medicine to see if it has any effect.

Randomization : This is like shaking things up in a fair way. You randomly put people into the control or experimental group so that each group is a good mix of different kinds of people. This helps make the results more reliable.

Sample : This is the group of people you're studying. They're a "sample" of a larger group that you're interested in. For instance, if you want to know how teenagers feel about a new video game, you might study a sample of 100 teenagers.

Bias : This is anything that might tilt your experiment one way or another without you realizing it. Like if you're testing a new kind of dog food and you only test it on poodles, that could create a bias because maybe poodles just really like that food and other breeds don't.

Data : This is the information you collect during the experiment. It's like the treasure you find on your journey of discovery!

Replication : This means doing the experiment more than once to make sure your findings hold up. It's like double-checking your answers on a test.

Hypothesis : This is your educated guess about what will happen in the experiment. It's like predicting the end of a movie based on the first half.

Steps of Experimental Design

Alright, let's say you're all fired up and ready to run your own experiment. Cool! But where do you start? Well, designing an experiment is a bit like planning a road trip. There are some key steps you've got to take to make sure you reach your destination. Let's break it down:

• Ask a Question : Before you hit the road, you've got to know where you're going. Same with experiments. You start with a question you want to answer, like "Does eating breakfast really make you do better in school?"
• Do Some Homework : Before you pack your bags, you look up the best places to visit, right? In science, this means reading up on what other people have already discovered about your topic.
• Form a Hypothesis : This is your educated guess about what you think will happen. It's like saying, "I bet this route will get us there faster."
• Plan the Details : Now you decide what kind of car you're driving (your experimental design), who's coming with you (your sample), and what snacks to bring (your variables).
• Randomization : Remember, this is like shuffling a deck of cards. You want to mix up who goes into your control and experimental groups to make sure it's a fair test.
• Run the Experiment : Finally, the rubber hits the road! You carry out your plan, making sure to collect your data carefully.
• Analyze the Data : Once the trip's over, you look at your photos and decide which ones are keepers. In science, this means looking at your data to see what it tells you.
• Draw Conclusions : Based on your data, did you find an answer to your question? This is like saying, "Yep, that route was faster," or "Nope, we hit a ton of traffic."
• Share Your Findings : After a great trip, you want to tell everyone about it, right? Scientists do the same by publishing their results so others can learn from them.
• Do It Again? : Sometimes one road trip just isn't enough. In the same way, scientists often repeat their experiments to make sure their findings are solid.

So there you have it! Those are the basic steps you need to follow when you're designing an experiment. Each step helps make sure that you're setting up a fair and reliable way to find answers to your big questions.

Let's get into examples of experimental designs.

1) True Experimental Design

In the world of experiments, the True Experimental Design is like the superstar quarterback everyone talks about. Born out of the early 20th-century work of statisticians like Ronald A. Fisher, this design is all about control, precision, and reliability.

Researchers carefully pick an independent variable to manipulate (remember, that's the thing they're changing on purpose) and measure the dependent variable (the effect they're studying). Then comes the magic trick—randomization. By randomly putting participants into either the control or experimental group, scientists make sure their experiment is as fair as possible.

No sneaky biases here!

True Experimental Design Pros

The pros of True Experimental Design are like the perks of a VIP ticket at a concert: you get the best and most trustworthy results. Because everything is controlled and randomized, you can feel pretty confident that the results aren't just a fluke.

True Experimental Design Cons

However, there's a catch. Sometimes, it's really tough to set up these experiments in a real-world situation. Imagine trying to control every single detail of your day, from the food you eat to the air you breathe. Not so easy, right?

True Experimental Design Uses

The fields that get the most out of True Experimental Designs are those that need super reliable results, like medical research.

When scientists were developing COVID-19 vaccines, they used this design to run clinical trials. They had control groups that received a placebo (a harmless substance with no effect) and experimental groups that got the actual vaccine. Then they measured how many people in each group got sick. By comparing the two, they could say, "Yep, this vaccine works!"

So next time you read about a groundbreaking discovery in medicine or technology, chances are a True Experimental Design was the VIP behind the scenes, making sure everything was on point. It's been the go-to for rigorous scientific inquiry for nearly a century, and it's not stepping off the stage anytime soon.

2) Quasi-Experimental Design

So, let's talk about the Quasi-Experimental Design. Think of this one as the cool cousin of True Experimental Design. It wants to be just like its famous relative, but it's a bit more laid-back and flexible. You'll find quasi-experimental designs when it's tricky to set up a full-blown True Experimental Design with all the bells and whistles.

Quasi-experiments still play with an independent variable, just like their stricter cousins. The big difference? They don't use randomization. It's like wanting to divide a bag of jelly beans equally between your friends, but you can't quite do it perfectly.

In real life, it's often not possible or ethical to randomly assign people to different groups, especially when dealing with sensitive topics like education or social issues. And that's where quasi-experiments come in.

Quasi-Experimental Design Pros

Even though they lack full randomization, quasi-experimental designs are like the Swiss Army knives of research: versatile and practical. They're especially popular in fields like education, sociology, and public policy.

For instance, when researchers wanted to figure out if the Head Start program , aimed at giving young kids a "head start" in school, was effective, they used a quasi-experimental design. They couldn't randomly assign kids to go or not go to preschool, but they could compare kids who did with kids who didn't.

Quasi-Experimental Design Cons

Of course, quasi-experiments come with their own bag of pros and cons. On the plus side, they're easier to set up and often cheaper than true experiments. But the flip side is that they're not as rock-solid in their conclusions. Because the groups aren't randomly assigned, there's always that little voice saying, "Hey, are we missing something here?"

Quasi-Experimental Design Uses

Quasi-Experimental Design gained traction in the mid-20th century. Researchers were grappling with real-world problems that didn't fit neatly into a laboratory setting. Plus, as society became more aware of ethical considerations, the need for flexible designs increased. So, the quasi-experimental approach was like a breath of fresh air for scientists wanting to study complex issues without a laundry list of restrictions.

In short, if True Experimental Design is the superstar quarterback, Quasi-Experimental Design is the versatile player who can adapt and still make significant contributions to the game.

3) Pre-Experimental Design

Now, let's talk about the Pre-Experimental Design. Imagine it as the beginner's skateboard you get before you try out for all the cool tricks. It has wheels, it rolls, but it's not built for the professional skatepark.

Similarly, pre-experimental designs give researchers a starting point. They let you dip your toes in the water of scientific research without diving in head-first.

So, what's the deal with pre-experimental designs?

Pre-Experimental Designs are the basic, no-frills versions of experiments. Researchers still mess around with an independent variable and measure a dependent variable, but they skip over the whole randomization thing and often don't even have a control group.

It's like baking a cake but forgetting the frosting and sprinkles; you'll get some results, but they might not be as complete or reliable as you'd like.

Pre-Experimental Design Pros

Why use such a simple setup? Because sometimes, you just need to get the ball rolling. Pre-experimental designs are great for quick-and-dirty research when you're short on time or resources. They give you a rough idea of what's happening, which you can use to plan more detailed studies later.

A good example of this is early studies on the effects of screen time on kids. Researchers couldn't control every aspect of a child's life, but they could easily ask parents to track how much time their kids spent in front of screens and then look for trends in behavior or school performance.

Pre-Experimental Design Cons

But here's the catch: pre-experimental designs are like that first draft of an essay. It helps you get your ideas down, but you wouldn't want to turn it in for a grade. Because these designs lack the rigorous structure of true or quasi-experimental setups, they can't give you rock-solid conclusions. They're more like clues or signposts pointing you in a certain direction.

Pre-Experimental Design Uses

This type of design became popular in the early stages of various scientific fields. Researchers used them to scratch the surface of a topic, generate some initial data, and then decide if it's worth exploring further. In other words, pre-experimental designs were the stepping stones that led to more complex, thorough investigations.

So, while Pre-Experimental Design may not be the star player on the team, it's like the practice squad that helps everyone get better. It's the starting point that can lead to bigger and better things.

4) Factorial Design

Now, buckle up, because we're moving into the world of Factorial Design, the multi-tasker of the experimental universe.

Imagine juggling not just one, but multiple balls in the air—that's what researchers do in a factorial design.

In Factorial Design, researchers are not satisfied with just studying one independent variable. Nope, they want to study two or more at the same time to see how they interact.

It's like cooking with several spices to see how they blend together to create unique flavors.

Factorial Design became the talk of the town with the rise of computers. Why? Because this design produces a lot of data, and computers are the number crunchers that help make sense of it all. So, thanks to our silicon friends, researchers can study complicated questions like, "How do diet AND exercise together affect weight loss?" instead of looking at just one of those factors.

Factorial Design Pros

This design's main selling point is its ability to explore interactions between variables. For instance, maybe a new study drug works really well for young people but not so great for older adults. A factorial design could reveal that age is a crucial factor, something you might miss if you only studied the drug's effectiveness in general. It's like being a detective who looks for clues not just in one room but throughout the entire house.

Factorial Design Cons

However, factorial designs have their own bag of challenges. First off, they can be pretty complicated to set up and run. Imagine coordinating a four-way intersection with lots of cars coming from all directions—you've got to make sure everything runs smoothly, or you'll end up with a traffic jam. Similarly, researchers need to carefully plan how they'll measure and analyze all the different variables.

Factorial Design Uses

Factorial designs are widely used in psychology to untangle the web of factors that influence human behavior. They're also popular in fields like marketing, where companies want to understand how different aspects like price, packaging, and advertising influence a product's success.

And speaking of success, the factorial design has been a hit since statisticians like Ronald A. Fisher (yep, him again!) expanded on it in the early-to-mid 20th century. It offered a more nuanced way of understanding the world, proving that sometimes, to get the full picture, you've got to juggle more than one ball at a time.

So, if True Experimental Design is the quarterback and Quasi-Experimental Design is the versatile player, Factorial Design is the strategist who sees the entire game board and makes moves accordingly.

5) Longitudinal Design

Alright, let's take a step into the world of Longitudinal Design. Picture it as the grand storyteller, the kind who doesn't just tell you about a single event but spins an epic tale that stretches over years or even decades. This design isn't about quick snapshots; it's about capturing the whole movie of someone's life or a long-running process.

You know how you might take a photo every year on your birthday to see how you've changed? Longitudinal Design is kind of like that, but for scientific research.

With Longitudinal Design, instead of measuring something just once, researchers come back again and again, sometimes over many years, to see how things are going. This helps them understand not just what's happening, but why it's happening and how it changes over time.

This design really started to shine in the latter half of the 20th century, when researchers began to realize that some questions can't be answered in a hurry. Think about studies that look at how kids grow up, or research on how a certain medicine affects you over a long period. These aren't things you can rush.

The famous Framingham Heart Study , started in 1948, is a prime example. It's been studying heart health in a small town in Massachusetts for decades, and the findings have shaped what we know about heart disease.

Longitudinal Design Pros

So, what's to love about Longitudinal Design? First off, it's the go-to for studying change over time, whether that's how people age or how a forest recovers from a fire.

Longitudinal Design Cons

But it's not all sunshine and rainbows. Longitudinal studies take a lot of patience and resources. Plus, keeping track of participants over many years can be like herding cats—difficult and full of surprises.

Longitudinal Design Uses

Despite these challenges, longitudinal studies have been key in fields like psychology, sociology, and medicine. They provide the kind of deep, long-term insights that other designs just can't match.

So, if the True Experimental Design is the superstar quarterback, and the Quasi-Experimental Design is the flexible athlete, then the Factorial Design is the strategist, and the Longitudinal Design is the wise elder who has seen it all and has stories to tell.

6) Cross-Sectional Design

Now, let's flip the script and talk about Cross-Sectional Design, the polar opposite of the Longitudinal Design. If Longitudinal is the grand storyteller, think of Cross-Sectional as the snapshot photographer. It captures a single moment in time, like a selfie that you take to remember a fun day. Researchers using this design collect all their data at one point, providing a kind of "snapshot" of whatever they're studying.

In a Cross-Sectional Design, researchers look at multiple groups all at the same time to see how they're different or similar.

This design rose to popularity in the mid-20th century, mainly because it's so quick and efficient. Imagine wanting to know how people of different ages feel about a new video game. Instead of waiting for years to see how opinions change, you could just ask people of all ages what they think right now. That's Cross-Sectional Design for you—fast and straightforward.

You'll find this type of research everywhere from marketing studies to healthcare. For instance, you might have heard about surveys asking people what they think about a new product or political issue. Those are usually cross-sectional studies, aimed at getting a quick read on public opinion.

Cross-Sectional Design Pros

So, what's the big deal with Cross-Sectional Design? Well, it's the go-to when you need answers fast and don't have the time or resources for a more complicated setup.

Cross-Sectional Design Cons

Remember, speed comes with trade-offs. While you get your results quickly, those results are stuck in time. They can't tell you how things change or why they're changing, just what's happening right now.

Cross-Sectional Design Uses

Also, because they're so quick and simple, cross-sectional studies often serve as the first step in research. They give scientists an idea of what's going on so they can decide if it's worth digging deeper. In that way, they're a bit like a movie trailer, giving you a taste of the action to see if you're interested in seeing the whole film.

So, in our lineup of experimental designs, if True Experimental Design is the superstar quarterback and Longitudinal Design is the wise elder, then Cross-Sectional Design is like the speedy running back—fast, agile, but not designed for long, drawn-out plays.

7) Correlational Design

Next on our roster is the Correlational Design, the keen observer of the experimental world. Imagine this design as the person at a party who loves people-watching. They don't interfere or get involved; they just observe and take mental notes about what's going on.

In a correlational study, researchers don't change or control anything; they simply observe and measure how two variables relate to each other.

The correlational design has roots in the early days of psychology and sociology. Pioneers like Sir Francis Galton used it to study how qualities like intelligence or height could be related within families.

This design is all about asking, "Hey, when this thing happens, does that other thing usually happen too?" For example, researchers might study whether students who have more study time get better grades or whether people who exercise more have lower stress levels.

One of the most famous correlational studies you might have heard of is the link between smoking and lung cancer. Back in the mid-20th century, researchers started noticing that people who smoked a lot also seemed to get lung cancer more often. They couldn't say smoking caused cancer—that would require a true experiment—but the strong correlation was a red flag that led to more research and eventually, health warnings.

Correlational Design Pros

This design is great at proving that two (or more) things can be related. Correlational designs can help prove that more detailed research is needed on a topic. They can help us see patterns or possible causes for things that we otherwise might not have realized.

Correlational Design Cons

But here's where you need to be careful: correlational designs can be tricky. Just because two things are related doesn't mean one causes the other. That's like saying, "Every time I wear my lucky socks, my team wins." Well, it's a fun thought, but those socks aren't really controlling the game.

Correlational Design Uses

Despite this limitation, correlational designs are popular in psychology, economics, and epidemiology, to name a few fields. They're often the first step in exploring a possible relationship between variables. Once a strong correlation is found, researchers may decide to conduct more rigorous experimental studies to examine cause and effect.

So, if the True Experimental Design is the superstar quarterback and the Longitudinal Design is the wise elder, the Factorial Design is the strategist, and the Cross-Sectional Design is the speedster, then the Correlational Design is the clever scout, identifying interesting patterns but leaving the heavy lifting of proving cause and effect to the other types of designs.

8) Meta-Analysis

Last but not least, let's talk about Meta-Analysis, the librarian of experimental designs.

If other designs are all about creating new research, Meta-Analysis is about gathering up everyone else's research, sorting it, and figuring out what it all means when you put it together.

Imagine a jigsaw puzzle where each piece is a different study. Meta-Analysis is the process of fitting all those pieces together to see the big picture.

The concept of Meta-Analysis started to take shape in the late 20th century, when computers became powerful enough to handle massive amounts of data. It was like someone handed researchers a super-powered magnifying glass, letting them examine multiple studies at the same time to find common trends or results.

You might have heard of the Cochrane Reviews in healthcare . These are big collections of meta-analyses that help doctors and policymakers figure out what treatments work best based on all the research that's been done.

For example, if ten different studies show that a certain medicine helps lower blood pressure, a meta-analysis would pull all that information together to give a more accurate answer.

Meta-Analysis Pros

The beauty of Meta-Analysis is that it can provide really strong evidence. Instead of relying on one study, you're looking at the whole landscape of research on a topic.

Meta-Analysis Cons

However, it does have some downsides. For one, Meta-Analysis is only as good as the studies it includes. If those studies are flawed, the meta-analysis will be too. It's like baking a cake: if you use bad ingredients, it doesn't matter how good your recipe is—the cake won't turn out well.

Meta-Analysis Uses

Despite these challenges, meta-analyses are highly respected and widely used in many fields like medicine, psychology, and education. They help us make sense of a world that's bursting with information by showing us the big picture drawn from many smaller snapshots.

So, in our all-star lineup, if True Experimental Design is the quarterback and Longitudinal Design is the wise elder, the Factorial Design is the strategist, the Cross-Sectional Design is the speedster, and the Correlational Design is the scout, then the Meta-Analysis is like the coach, using insights from everyone else's plays to come up with the best game plan.

9) Non-Experimental Design

Now, let's talk about a player who's a bit of an outsider on this team of experimental designs—the Non-Experimental Design. Think of this design as the commentator or the journalist who covers the game but doesn't actually play.

In a Non-Experimental Design, researchers are like reporters gathering facts, but they don't interfere or change anything. They're simply there to describe and analyze.

Non-Experimental Design Pros

So, what's the deal with Non-Experimental Design? Its strength is in description and exploration. It's really good for studying things as they are in the real world, without changing any conditions.

Non-Experimental Design Cons

Because a non-experimental design doesn't manipulate variables, it can't prove cause and effect. It's like a weather reporter: they can tell you it's raining, but they can't tell you why it's raining.

The downside? Since researchers aren't controlling variables, it's hard to rule out other explanations for what they observe. It's like hearing one side of a story—you get an idea of what happened, but it might not be the complete picture.

Non-Experimental Design Uses

Non-Experimental Design has always been a part of research, especially in fields like anthropology, sociology, and some areas of psychology.

For instance, if you've ever heard of studies that describe how people behave in different cultures or what teens like to do in their free time, that's often Non-Experimental Design at work. These studies aim to capture the essence of a situation, like painting a portrait instead of taking a snapshot.

One well-known example you might have heard about is the Kinsey Reports from the 1940s and 1950s, which described sexual behavior in men and women. Researchers interviewed thousands of people but didn't manipulate any variables like you would in a true experiment. They simply collected data to create a comprehensive picture of the subject matter.

So, in our metaphorical team of research designs, if True Experimental Design is the quarterback and Longitudinal Design is the wise elder, Factorial Design is the strategist, Cross-Sectional Design is the speedster, Correlational Design is the scout, and Meta-Analysis is the coach, then Non-Experimental Design is the sports journalist—always present, capturing the game, but not part of the action itself.

10) Repeated Measures Design

Time to meet the Repeated Measures Design, the time traveler of our research team. If this design were a player in a sports game, it would be the one who keeps revisiting past plays to figure out how to improve the next one.

Repeated Measures Design is all about studying the same people or subjects multiple times to see how they change or react under different conditions.

The idea behind Repeated Measures Design isn't new; it's been around since the early days of psychology and medicine. You could say it's a cousin to the Longitudinal Design, but instead of looking at how things naturally change over time, it focuses on how the same group reacts to different things.

Imagine a study looking at how a new energy drink affects people's running speed. Instead of comparing one group that drank the energy drink to another group that didn't, a Repeated Measures Design would have the same group of people run multiple times—once with the energy drink, and once without. This way, you're really zeroing in on the effect of that energy drink, making the results more reliable.

Repeated Measures Design Pros

The strong point of Repeated Measures Design is that it's super focused. Because it uses the same subjects, you don't have to worry about differences between groups messing up your results.

Repeated Measures Design Cons

But the downside? Well, people can get tired or bored if they're tested too many times, which might affect how they respond.

Repeated Measures Design Uses

A famous example of this design is the "Little Albert" experiment, conducted by John B. Watson and Rosalie Rayner in 1920. In this study, a young boy was exposed to a white rat and other stimuli several times to see how his emotional responses changed. Though the ethical standards of this experiment are often criticized today, it was groundbreaking in understanding conditioned emotional responses.

In our metaphorical lineup of research designs, if True Experimental Design is the quarterback and Longitudinal Design is the wise elder, Factorial Design is the strategist, Cross-Sectional Design is the speedster, Correlational Design is the scout, Meta-Analysis is the coach, and Non-Experimental Design is the journalist, then Repeated Measures Design is the time traveler—always looping back to fine-tune the game plan.

11) Crossover Design

Next up is Crossover Design, the switch-hitter of the research world. If you're familiar with baseball, you'll know a switch-hitter is someone who can bat both right-handed and left-handed.

In a similar way, Crossover Design allows subjects to experience multiple conditions, flipping them around so that everyone gets a turn in each role.

This design is like the utility player on our team—versatile, flexible, and really good at adapting.

The Crossover Design has its roots in medical research and has been popular since the mid-20th century. It's often used in clinical trials to test the effectiveness of different treatments.

Crossover Design Pros

The neat thing about this design is that it allows each participant to serve as their own control group. Imagine you're testing two new kinds of headache medicine. Instead of giving one type to one group and another type to a different group, you'd give both kinds to the same people but at different times.

Crossover Design Cons

What's the big deal with Crossover Design? Its major strength is in reducing the "noise" that comes from individual differences. Since each person experiences all conditions, it's easier to see real effects. However, there's a catch. This design assumes that there's no lasting effect from the first condition when you switch to the second one. That might not always be true. If the first treatment has a long-lasting effect, it could mess up the results when you switch to the second treatment.

Crossover Design Uses

A well-known example of Crossover Design is in studies that look at the effects of different types of diets—like low-carb vs. low-fat diets. Researchers might have participants follow a low-carb diet for a few weeks, then switch them to a low-fat diet. By doing this, they can more accurately measure how each diet affects the same group of people.

In our team of experimental designs, if True Experimental Design is the quarterback and Longitudinal Design is the wise elder, Factorial Design is the strategist, Cross-Sectional Design is the speedster, Correlational Design is the scout, Meta-Analysis is the coach, Non-Experimental Design is the journalist, and Repeated Measures Design is the time traveler, then Crossover Design is the versatile utility player—always ready to adapt and play multiple roles to get the most accurate results.

12) Cluster Randomized Design

Meet the Cluster Randomized Design, the team captain of group-focused research. In our imaginary lineup of experimental designs, if other designs focus on individual players, then Cluster Randomized Design is looking at how the entire team functions.

This approach is especially common in educational and community-based research, and it's been gaining traction since the late 20th century.

Here's how Cluster Randomized Design works: Instead of assigning individual people to different conditions, researchers assign entire groups, or "clusters." These could be schools, neighborhoods, or even entire towns. This helps you see how the new method works in a real-world setting.

Imagine you want to see if a new anti-bullying program really works. Instead of selecting individual students, you'd introduce the program to a whole school or maybe even several schools, and then compare the results to schools without the program.

Cluster Randomized Design Pros

Why use Cluster Randomized Design? Well, sometimes it's just not practical to assign conditions at the individual level. For example, you can't really have half a school following a new reading program while the other half sticks with the old one; that would be way too confusing! Cluster Randomization helps get around this problem by treating each "cluster" as its own mini-experiment.

Cluster Randomized Design Cons

There's a downside, too. Because entire groups are assigned to each condition, there's a risk that the groups might be different in some important way that the researchers didn't account for. That's like having one sports team that's full of veterans playing against a team of rookies; the match wouldn't be fair.

Cluster Randomized Design Uses

A famous example is the research conducted to test the effectiveness of different public health interventions, like vaccination programs. Researchers might roll out a vaccination program in one community but not in another, then compare the rates of disease in both.

In our metaphorical research team, if True Experimental Design is the quarterback, Longitudinal Design is the wise elder, Factorial Design is the strategist, Cross-Sectional Design is the speedster, Correlational Design is the scout, Meta-Analysis is the coach, Non-Experimental Design is the journalist, Repeated Measures Design is the time traveler, and Crossover Design is the utility player, then Cluster Randomized Design is the team captain—always looking out for the group as a whole.

13) Mixed-Methods Design

Say hello to Mixed-Methods Design, the all-rounder or the "Renaissance player" of our research team.

Mixed-Methods Design uses a blend of both qualitative and quantitative methods to get a more complete picture, just like a Renaissance person who's good at lots of different things. It's like being good at both offense and defense in a sport; you've got all your bases covered!

Mixed-Methods Design is a fairly new kid on the block, becoming more popular in the late 20th and early 21st centuries as researchers began to see the value in using multiple approaches to tackle complex questions. It's the Swiss Army knife in our research toolkit, combining the best parts of other designs to be more versatile.

Here's how it could work: Imagine you're studying the effects of a new educational app on students' math skills. You might use quantitative methods like tests and grades to measure how much the students improve—that's the 'numbers part.'

But you also want to know how the students feel about math now, or why they think they got better or worse. For that, you could conduct interviews or have students fill out journals—that's the 'story part.'

Mixed-Methods Design Pros

So, what's the scoop on Mixed-Methods Design? The strength is its versatility and depth; you're not just getting numbers or stories, you're getting both, which gives a fuller picture.

Mixed-Methods Design Cons

But, it's also more challenging. Imagine trying to play two sports at the same time! You have to be skilled in different research methods and know how to combine them effectively.

Mixed-Methods Design Uses

A high-profile example of Mixed-Methods Design is research on climate change. Scientists use numbers and data to show temperature changes (quantitative), but they also interview people to understand how these changes are affecting communities (qualitative).

In our team of experimental designs, if True Experimental Design is the quarterback, Longitudinal Design is the wise elder, Factorial Design is the strategist, Cross-Sectional Design is the speedster, Correlational Design is the scout, Meta-Analysis is the coach, Non-Experimental Design is the journalist, Repeated Measures Design is the time traveler, Crossover Design is the utility player, and Cluster Randomized Design is the team captain, then Mixed-Methods Design is the Renaissance player—skilled in multiple areas and able to bring them all together for a winning strategy.

14) Multivariate Design

Now, let's turn our attention to Multivariate Design, the multitasker of the research world.

If our lineup of research designs were like players on a basketball court, Multivariate Design would be the player dribbling, passing, and shooting all at once. This design doesn't just look at one or two things; it looks at several variables simultaneously to see how they interact and affect each other.

Multivariate Design is like baking a cake with many ingredients. Instead of just looking at how flour affects the cake, you also consider sugar, eggs, and milk all at once. This way, you understand how everything works together to make the cake taste good or bad.

Multivariate Design has been a go-to method in psychology, economics, and social sciences since the latter half of the 20th century. With the advent of computers and advanced statistical software, analyzing multiple variables at once became a lot easier, and Multivariate Design soared in popularity.

Multivariate Design Pros

So, what's the benefit of using Multivariate Design? Its power lies in its complexity. By studying multiple variables at the same time, you can get a really rich, detailed understanding of what's going on.

Multivariate Design Cons

But that complexity can also be a drawback. With so many variables, it can be tough to tell which ones are really making a difference and which ones are just along for the ride.

Multivariate Design Uses

Imagine you're a coach trying to figure out the best strategy to win games. You wouldn't just look at how many points your star player scores; you'd also consider assists, rebounds, turnovers, and maybe even how loud the crowd is. A Multivariate Design would help you understand how all these factors work together to determine whether you win or lose.

A well-known example of Multivariate Design is in market research. Companies often use this approach to figure out how different factors—like price, packaging, and advertising—affect sales. By studying multiple variables at once, they can find the best combination to boost profits.

In our metaphorical research team, if True Experimental Design is the quarterback, Longitudinal Design is the wise elder, Factorial Design is the strategist, Cross-Sectional Design is the speedster, Correlational Design is the scout, Meta-Analysis is the coach, Non-Experimental Design is the journalist, Repeated Measures Design is the time traveler, Crossover Design is the utility player, Cluster Randomized Design is the team captain, and Mixed-Methods Design is the Renaissance player, then Multivariate Design is the multitasker—juggling many variables at once to get a fuller picture of what's happening.

15) Pretest-Posttest Design

Let's introduce Pretest-Posttest Design, the "Before and After" superstar of our research team. You've probably seen those before-and-after pictures in ads for weight loss programs or home renovations, right?

Well, this design is like that, but for science! Pretest-Posttest Design checks out what things are like before the experiment starts and then compares that to what things are like after the experiment ends.

This design is one of the classics, a staple in research for decades across various fields like psychology, education, and healthcare. It's so simple and straightforward that it has stayed popular for a long time.

In Pretest-Posttest Design, you measure your subject's behavior or condition before you introduce any changes—that's your "before" or "pretest." Then you do your experiment, and after it's done, you measure the same thing again—that's your "after" or "posttest."

Pretest-Posttest Design Pros

What makes Pretest-Posttest Design special? It's pretty easy to understand and doesn't require fancy statistics.

Pretest-Posttest Design Cons

But there are some pitfalls. For example, what if the kids in our math example get better at multiplication just because they're older or because they've taken the test before? That would make it hard to tell if the program is really effective or not.

Pretest-Posttest Design Uses

Let's say you're a teacher and you want to know if a new math program helps kids get better at multiplication. First, you'd give all the kids a multiplication test—that's your pretest. Then you'd teach them using the new math program. At the end, you'd give them the same test again—that's your posttest. If the kids do better on the second test, you might conclude that the program works.

One famous use of Pretest-Posttest Design is in evaluating the effectiveness of driver's education courses. Researchers will measure people's driving skills before and after the course to see if they've improved.

16) Solomon Four-Group Design

Next up is the Solomon Four-Group Design, the "chess master" of our research team. This design is all about strategy and careful planning. Named after Richard L. Solomon who introduced it in the 1940s, this method tries to correct some of the weaknesses in simpler designs, like the Pretest-Posttest Design.

Here's how it rolls: The Solomon Four-Group Design uses four different groups to test a hypothesis. Two groups get a pretest, then one of them receives the treatment or intervention, and both get a posttest. The other two groups skip the pretest, and only one of them receives the treatment before they both get a posttest.

Sound complicated? It's like playing 4D chess; you're thinking several moves ahead!

Solomon Four-Group Design Pros

What's the pro and con of the Solomon Four-Group Design? On the plus side, it provides really robust results because it accounts for so many variables.

Solomon Four-Group Design Cons

The downside? It's a lot of work and requires a lot of participants, making it more time-consuming and costly.

Solomon Four-Group Design Uses

Let's say you want to figure out if a new way of teaching history helps students remember facts better. Two classes take a history quiz (pretest), then one class uses the new teaching method while the other sticks with the old way. Both classes take another quiz afterward (posttest).

Meanwhile, two more classes skip the initial quiz, and then one uses the new method before both take the final quiz. Comparing all four groups will give you a much clearer picture of whether the new teaching method works and whether the pretest itself affects the outcome.

The Solomon Four-Group Design is less commonly used than simpler designs but is highly respected for its ability to control for more variables. It's a favorite in educational and psychological research where you really want to dig deep and figure out what's actually causing changes.

17) Adaptive Designs

Now, let's talk about Adaptive Designs, the chameleons of the experimental world.

Imagine you're a detective, and halfway through solving a case, you find a clue that changes everything. You wouldn't just stick to your old plan; you'd adapt and change your approach, right? That's exactly what Adaptive Designs allow researchers to do.

In an Adaptive Design, researchers can make changes to the study as it's happening, based on early results. In a traditional study, once you set your plan, you stick to it from start to finish.

Adaptive Design Pros

This method is particularly useful in fast-paced or high-stakes situations, like developing a new vaccine in the middle of a pandemic. The ability to adapt can save both time and resources, and more importantly, it can save lives by getting effective treatments out faster.

Adaptive Design Cons

But Adaptive Designs aren't without their drawbacks. They can be very complex to plan and carry out, and there's always a risk that the changes made during the study could introduce bias or errors.

Adaptive Design Uses

Adaptive Designs are most often seen in clinical trials, particularly in the medical and pharmaceutical fields.

For instance, if a new drug is showing really promising results, the study might be adjusted to give more participants the new treatment instead of a placebo. Or if one dose level is showing bad side effects, it might be dropped from the study.

The best part is, these changes are pre-planned. Researchers lay out in advance what changes might be made and under what conditions, which helps keep everything scientific and above board.

In terms of applications, besides their heavy usage in medical and pharmaceutical research, Adaptive Designs are also becoming increasingly popular in software testing and market research. In these fields, being able to quickly adjust to early results can give companies a significant advantage.

Adaptive Designs are like the agile startups of the research world—quick to pivot, keen to learn from ongoing results, and focused on rapid, efficient progress. However, they require a great deal of expertise and careful planning to ensure that the adaptability doesn't compromise the integrity of the research.

18) Bayesian Designs

Next, let's dive into Bayesian Designs, the data detectives of the research universe. Named after Thomas Bayes, an 18th-century statistician and minister, this design doesn't just look at what's happening now; it also takes into account what's happened before.

Imagine if you were a detective who not only looked at the evidence in front of you but also used your past cases to make better guesses about your current one. That's the essence of Bayesian Designs.

Bayesian Designs are like detective work in science. As you gather more clues (or data), you update your best guess on what's really happening. This way, your experiment gets smarter as it goes along.

In the world of research, Bayesian Designs are most notably used in areas where you have some prior knowledge that can inform your current study. For example, if earlier research shows that a certain type of medicine usually works well for a specific illness, a Bayesian Design would include that information when studying a new group of patients with the same illness.

Bayesian Design Pros

One of the major advantages of Bayesian Designs is their efficiency. Because they use existing data to inform the current experiment, often fewer resources are needed to reach a reliable conclusion.

Bayesian Design Cons

However, they can be quite complicated to set up and require a deep understanding of both statistics and the subject matter at hand.

Bayesian Design Uses

Bayesian Designs are highly valued in medical research, finance, environmental science, and even in Internet search algorithms. Their ability to continually update and refine hypotheses based on new evidence makes them particularly useful in fields where data is constantly evolving and where quick, informed decisions are crucial.

Here's a real-world example: In the development of personalized medicine, where treatments are tailored to individual patients, Bayesian Designs are invaluable. If a treatment has been effective for patients with similar genetics or symptoms in the past, a Bayesian approach can use that data to predict how well it might work for a new patient.

This type of design is also increasingly popular in machine learning and artificial intelligence. In these fields, Bayesian Designs help algorithms "learn" from past data to make better predictions or decisions in new situations. It's like teaching a computer to be a detective that gets better and better at solving puzzles the more puzzles it sees.

19) Covariate Adaptive Randomization

Now let's turn our attention to Covariate Adaptive Randomization, which you can think of as the "matchmaker" of experimental designs.

Picture a soccer coach trying to create the most balanced teams for a friendly match. They wouldn't just randomly assign players; they'd take into account each player's skills, experience, and other traits.

Covariate Adaptive Randomization is all about creating the most evenly matched groups possible for an experiment.

In traditional randomization, participants are allocated to different groups purely by chance. This is a pretty fair way to do things, but it can sometimes lead to unbalanced groups.

Imagine if all the professional-level players ended up on one soccer team and all the beginners on another; that wouldn't be a very informative match! Covariate Adaptive Randomization fixes this by using important traits or characteristics (called "covariates") to guide the randomization process.

Covariate Adaptive Randomization Pros

The benefits of this design are pretty clear: it aims for balance and fairness, making the final results more trustworthy.

Covariate Adaptive Randomization Cons

But it's not perfect. It can be complex to implement and requires a deep understanding of which characteristics are most important to balance.

Covariate Adaptive Randomization Uses

This design is particularly useful in medical trials. Let's say researchers are testing a new medication for high blood pressure. Participants might have different ages, weights, or pre-existing conditions that could affect the results.

Covariate Adaptive Randomization would make sure that each treatment group has a similar mix of these characteristics, making the results more reliable and easier to interpret.

In practical terms, this design is often seen in clinical trials for new drugs or therapies, but its principles are also applicable in fields like psychology, education, and social sciences.

For instance, in educational research, it might be used to ensure that classrooms being compared have similar distributions of students in terms of academic ability, socioeconomic status, and other factors.

Covariate Adaptive Randomization is like the wise elder of the group, ensuring that everyone has an equal opportunity to show their true capabilities, thereby making the collective results as reliable as possible.

20) Stepped Wedge Design

Let's now focus on the Stepped Wedge Design, a thoughtful and cautious member of the experimental design family.

Imagine you're trying out a new gardening technique, but you're not sure how well it will work. You decide to apply it to one section of your garden first, watch how it performs, and then gradually extend the technique to other sections. This way, you get to see its effects over time and across different conditions. That's basically how Stepped Wedge Design works.

In a Stepped Wedge Design, all participants or clusters start off in the control group, and then, at different times, they 'step' over to the intervention or treatment group. This creates a wedge-like pattern over time where more and more participants receive the treatment as the study progresses. It's like rolling out a new policy in phases, monitoring its impact at each stage before extending it to more people.

Stepped Wedge Design Pros

The Stepped Wedge Design offers several advantages. Firstly, it allows for the study of interventions that are expected to do more good than harm, which makes it ethically appealing.

Secondly, it's useful when resources are limited and it's not feasible to roll out a new treatment to everyone at once. Lastly, because everyone eventually receives the treatment, it can be easier to get buy-in from participants or organizations involved in the study.

Stepped Wedge Design Cons

However, this design can be complex to analyze because it has to account for both the time factor and the changing conditions in each 'step' of the wedge. And like any study where participants know they're receiving an intervention, there's the potential for the results to be influenced by the placebo effect or other biases.

Stepped Wedge Design Uses

This design is particularly useful in health and social care research. For instance, if a hospital wants to implement a new hygiene protocol, it might start in one department, assess its impact, and then roll it out to other departments over time. This allows the hospital to adjust and refine the new protocol based on real-world data before it's fully implemented.

In terms of applications, Stepped Wedge Designs are commonly used in public health initiatives, organizational changes in healthcare settings, and social policy trials. They are particularly useful in situations where an intervention is being rolled out gradually and it's important to understand its impacts at each stage.

21) Sequential Design

Next up is Sequential Design, the dynamic and flexible member of our experimental design family.

Imagine you're playing a video game where you can choose different paths. If you take one path and find a treasure chest, you might decide to continue in that direction. If you hit a dead end, you might backtrack and try a different route. Sequential Design operates in a similar fashion, allowing researchers to make decisions at different stages based on what they've learned so far.

In a Sequential Design, the experiment is broken down into smaller parts, or "sequences." After each sequence, researchers pause to look at the data they've collected. Based on those findings, they then decide whether to stop the experiment because they've got enough information, or to continue and perhaps even modify the next sequence.

Sequential Design Pros

This allows for a more efficient use of resources, as you're only continuing with the experiment if the data suggests it's worth doing so.

One of the great things about Sequential Design is its efficiency. Because you're making data-driven decisions along the way, you can often reach conclusions more quickly and with fewer resources.

Sequential Design Cons

However, it requires careful planning and expertise to ensure that these "stop or go" decisions are made correctly and without bias.

Sequential Design Uses

In terms of its applications, besides healthcare and medicine, Sequential Design is also popular in quality control in manufacturing, environmental monitoring, and financial modeling. In these areas, being able to make quick decisions based on incoming data can be a big advantage.

This design is often used in clinical trials involving new medications or treatments. For example, if early results show that a new drug has significant side effects, the trial can be stopped before more people are exposed to it.

On the flip side, if the drug is showing promising results, the trial might be expanded to include more participants or to extend the testing period.

Think of Sequential Design as the nimble athlete of experimental designs, capable of quick pivots and adjustments to reach the finish line in the most effective way possible. But just like an athlete needs a good coach, this design requires expert oversight to make sure it stays on the right track.

22) Field Experiments

Last but certainly not least, let's explore Field Experiments—the adventurers of the experimental design world.

Picture a scientist leaving the controlled environment of a lab to test a theory in the real world, like a biologist studying animals in their natural habitat or a social scientist observing people in a real community. These are Field Experiments, and they're all about getting out there and gathering data in real-world settings.

Field Experiments embrace the messiness of the real world, unlike laboratory experiments, where everything is controlled down to the smallest detail. This makes them both exciting and challenging.

Field Experiment Pros

On one hand, the results often give us a better understanding of how things work outside the lab.

While Field Experiments offer real-world relevance, they come with challenges like controlling for outside factors and the ethical considerations of intervening in people's lives without their knowledge.

Field Experiment Cons

On the other hand, the lack of control can make it harder to tell exactly what's causing what. Yet, despite these challenges, they remain a valuable tool for researchers who want to understand how theories play out in the real world.

Field Experiment Uses

Let's say a school wants to improve student performance. In a Field Experiment, they might change the school's daily schedule for one semester and keep track of how students perform compared to another school where the schedule remained the same.

Because the study is happening in a real school with real students, the results could be very useful for understanding how the change might work in other schools. But since it's the real world, lots of other factors—like changes in teachers or even the weather—could affect the results.

Field Experiments are widely used in economics, psychology, education, and public policy. For example, you might have heard of the famous "Broken Windows" experiment in the 1980s that looked at how small signs of disorder, like broken windows or graffiti, could encourage more serious crime in neighborhoods. This experiment had a big impact on how cities think about crime prevention.

From the foundational concepts of control groups and independent variables to the sophisticated layouts like Covariate Adaptive Randomization and Sequential Design, it's clear that the realm of experimental design is as varied as it is fascinating.

We've seen that each design has its own special talents, ideal for specific situations. Some designs, like the Classic Controlled Experiment, are like reliable old friends you can always count on.

Others, like Sequential Design, are flexible and adaptable, making quick changes based on what they learn. And let's not forget the adventurous Field Experiments, which take us out of the lab and into the real world to discover things we might not see otherwise.

Choosing the right experimental design is like picking the right tool for the job. The method you choose can make a big difference in how reliable your results are and how much people will trust what you've discovered. And as we've learned, there's a design to suit just about every question, every problem, and every curiosity.

So the next time you read about a new discovery in medicine, psychology, or any other field, you'll have a better understanding of the thought and planning that went into figuring things out. Experimental design is more than just a set of rules; it's a structured way to explore the unknown and answer questions that can change the world.

Related posts:

• Experimental Psychologist Career (Salary + Duties + Interviews)
• 40+ Famous Psychologists (Images + Biographies)
• 11+ Psychology Experiment Ideas (Goals + Methods)
• The Little Albert Experiment
• 41+ White Collar Job Examples (Salary + Path)

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15 Famous Experiments and Case Studies in Psychology

Chris Drew (PhD)

Dr. Chris Drew is the founder of the Helpful Professor. He holds a PhD in education and has published over 20 articles in scholarly journals. He is the former editor of the Journal of Learning Development in Higher Education. [Image Descriptor: Photo of Chris]

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Psychology has seen thousands upon thousands of research studies over the years. Most of these studies have helped shape our current understanding of human thoughts, behavior, and feelings.

The psychology case studies in this list are considered classic examples of psychological case studies and experiments, which are still being taught in introductory psychology courses up to this day.

Some studies, however, were downright shocking and controversial that you’d probably wonder why such studies were conducted back in the day. Imagine participating in an experiment for a small reward or extra class credit, only to be left scarred for life. These kinds of studies, however, paved the way for a more ethical approach to studying psychology and implementation of research standards such as the use of debriefing in psychology research .

Case Study vs. Experiment

Before we dive into the list of the most famous studies in psychology, let us first review the difference between case studies and experiments.

• It is an in-depth study and analysis of an individual, group, community, or phenomenon. The results of a case study cannot be applied to the whole population, but they can provide insights for further studies.
• It often uses qualitative research methods such as observations, surveys, and interviews.
• It is often conducted in real-life settings rather than in controlled environments.
• An experiment is a type of study done on a sample or group of random participants, the results of which can be generalized to the whole population.
• It often uses quantitative research methods that rely on numbers and statistics.
• It is conducted in controlled environments, wherein some things or situations are manipulated.

See Also: Experimental vs Observational Studies

Famous Experiments in Psychology

1. the marshmallow experiment.

Psychologist Walter Mischel conducted the marshmallow experiment at Stanford University in the 1960s to early 1970s. It was a simple test that aimed to define the connection between delayed gratification and success in life.

The instructions were fairly straightforward: children ages 4-6 were presented a piece of marshmallow on a table and they were told that they would receive a second piece if they could wait for 15 minutes without eating the first marshmallow.

About one-third of the 600 participants succeeded in delaying gratification to receive the second marshmallow. Mischel and his team followed up on these participants in the 1990s, learning that those who had the willpower to wait for a larger reward experienced more success in life in terms of SAT scores and other metrics.

This case study also supported self-control theory , a theory in criminology that holds that people with greater self-control are less likely to end up in trouble with the law!

The classic marshmallow experiment, however, was debunked in a 2018 replication study done by Tyler Watts and colleagues.

This more recent experiment had a larger group of participants (900) and a better representation of the general population when it comes to race and ethnicity. In this study, the researchers found out that the ability to wait for a second marshmallow does not depend on willpower alone but more so on the economic background and social status of the participants.

2. The Bystander Effect

In 1694, Kitty Genovese was murdered in the neighborhood of Kew Gardens, New York. It was told that there were up to 38 witnesses and onlookers in the vicinity of the crime scene, but nobody did anything to stop the murder or call for help.

Such tragedy was the catalyst that inspired social psychologists Bibb Latane and John Darley to formulate the phenomenon called bystander effect or bystander apathy .

Subsequent investigations showed that this story was exaggerated and inaccurate, as there were actually only about a dozen witnesses, at least two of whom called the police. But the case of Kitty Genovese led to various studies that aim to shed light on the bystander phenomenon.

Latane and Darley tested bystander intervention in an experimental study . Participants were asked to answer a questionnaire inside a room, and they would either be alone or with two other participants (who were actually actors or confederates in the study). Smoke would then come out from under the door. The reaction time of participants was tested — how long would it take them to report the smoke to the authorities or the experimenters?

The results showed that participants who were alone in the room reported the smoke faster than participants who were with two passive others. The study suggests that the more onlookers are present in an emergency situation, the less likely someone would step up to help, a social phenomenon now popularly called the bystander effect.

3. Asch Conformity Study

Have you ever made a decision against your better judgment just to fit in with your friends or family? The Asch Conformity Studies will help you understand this kind of situation better.

In this experiment, a group of participants were shown three numbered lines of different lengths and asked to identify the longest of them all. However, only one true participant was present in every group and the rest were actors, most of whom told the wrong answer.

Results showed that the participants went for the wrong answer, even though they knew which line was the longest one in the first place. When the participants were asked why they identified the wrong one, they said that they didn’t want to be branded as strange or peculiar.

This study goes to show that there are situations in life when people prefer fitting in than being right. It also tells that there is power in numbers — a group’s decision can overwhelm a person and make them doubt their judgment.

4. The Bobo Doll Experiment

The Bobo Doll Experiment was conducted by Dr. Albert Bandura, the proponent of social learning theory .

Back in the 1960s, the Nature vs. Nurture debate was a popular topic among psychologists. Bandura contributed to this discussion by proposing that human behavior is mostly influenced by environmental rather than genetic factors.

In the Bobo Doll Experiment, children were divided into three groups: one group was shown a video in which an adult acted aggressively toward the Bobo Doll, the second group was shown a video in which an adult play with the Bobo Doll, and the third group served as the control group where no video was shown.

The children were then led to a room with different kinds of toys, including the Bobo Doll they’ve seen in the video. Results showed that children tend to imitate the adults in the video. Those who were presented the aggressive model acted aggressively toward the Bobo Doll while those who were presented the passive model showed less aggression.

While the Bobo Doll Experiment can no longer be replicated because of ethical concerns, it has laid out the foundations of social learning theory and helped us understand the degree of influence adult behavior has on children.

5. Blue Eye / Brown Eye Experiment

Following the assassination of Martin Luther King Jr. in 1968, third-grade teacher Jane Elliott conducted an experiment in her class. Although not a formal experiment in controlled settings, A Class Divided is a good example of a social experiment to help children understand the concept of racism and discrimination.

The class was divided into two groups: blue-eyed children and brown-eyed children. For one day, Elliott gave preferential treatment to her blue-eyed students, giving them more attention and pampering them with rewards. The next day, it was the brown-eyed students’ turn to receive extra favors and privileges.

As a result, whichever group of students was given preferential treatment performed exceptionally well in class, had higher quiz scores, and recited more frequently; students who were discriminated against felt humiliated, answered poorly in tests, and became uncertain with their answers in class.

This study is now widely taught in sociocultural psychology classes.

6. Stanford Prison Experiment

One of the most controversial and widely-cited studies in psychology is the Stanford Prison Experiment , conducted by Philip Zimbardo at the basement of the Stanford psychology building in 1971. The hypothesis was that abusive behavior in prisons is influenced by the personality traits of the prisoners and prison guards.

The participants in the experiment were college students who were randomly assigned as either a prisoner or a prison guard. The prison guards were then told to run the simulated prison for two weeks. However, the experiment had to be stopped in just 6 days.

The prison guards abused their authority and harassed the prisoners through verbal and physical means. The prisoners, on the other hand, showed submissive behavior. Zimbardo decided to stop the experiment because the prisoners were showing signs of emotional and physical breakdown.

Although the experiment wasn’t completed, the results strongly showed that people can easily get into a social role when others expect them to, especially when it’s highly stereotyped .

7. The Halo Effect

Have you ever wondered why toothpastes and other dental products are endorsed in advertisements by celebrities more often than dentists? The Halo Effect is one of the reasons!

The Halo Effect shows how one favorable attribute of a person can gain them positive perceptions in other attributes. In the case of product advertisements, attractive celebrities are also perceived as intelligent and knowledgeable of a certain subject matter even though they’re not technically experts.

The Halo Effect originated in a classic study done by Edward Thorndike in the early 1900s. He asked military commanding officers to rate their subordinates based on different qualities, such as physical appearance, leadership, dependability, and intelligence.

The results showed that high ratings of a particular quality influences the ratings of other qualities, producing a halo effect of overall high ratings. The opposite also applied, which means that a negative rating in one quality also correlated to negative ratings in other qualities.

Experiments on the Halo Effect came in various formats as well, supporting Thorndike’s original theory. This phenomenon suggests that our perception of other people’s overall personality is hugely influenced by a quality that we focus on.

8. Cognitive Dissonance

There are experiences in our lives when our beliefs and behaviors do not align with each other and we try to justify them in our minds. This is cognitive dissonance , which was studied in an experiment by Leon Festinger and James Carlsmith back in 1959.

In this experiment, participants had to go through a series of boring and repetitive tasks, such as spending an hour turning pegs in a wooden knob. After completing the tasks, they were then paid either $1 or $20 to tell the next participants that the tasks were extremely fun and enjoyable. Afterwards, participants were asked to rate the experiment. Those who were given $1 rated the experiment as more interesting and fun than those who received $20.

The results showed that those who received a smaller incentive to lie experienced cognitive dissonance — $1 wasn’t enough incentive for that one hour of painstakingly boring activity, so the participants had to justify that they had fun anyway.

Famous Case Studies in Psychology

9. little albert.

In 1920, behaviourist theorists John Watson and Rosalie Rayner experimented on a 9-month-old baby to test the effects of classical conditioning in instilling fear in humans.

This was such a controversial study that it gained popularity in psychology textbooks and syllabi because it is a classic example of unethical research studies done in the name of science.

In one of the experiments, Little Albert was presented with a harmless stimulus or object, a white rat, which he wasn’t scared of at first. But every time Little Albert would see the white rat, the researchers would play a scary sound of hammer and steel. After about 6 pairings, Little Albert learned to fear the rat even without the scary sound.

Little Albert developed signs of fear to different objects presented to him through classical conditioning . He even generalized his fear to other stimuli not present in the course of the experiment.

10. Phineas Gage

Phineas Gage is such a celebrity in Psych 101 classes, even though the way he rose to popularity began with a tragic accident. He was a resident of Central Vermont and worked in the construction of a new railway line in the mid-1800s. One day, an explosive went off prematurely, sending a tamping iron straight into his face and through his brain.

Gage survived the accident, fortunately, something that is considered a feat even up to this day. He managed to find a job as a stagecoach after the accident. However, his family and friends reported that his personality changed so much that “he was no longer Gage” (Harlow, 1868).

New evidence on the case of Phineas Gage has since come to light, thanks to modern scientific studies and medical tests. However, there are still plenty of mysteries revolving around his brain damage and subsequent recovery.

11. Anna O.

Anna O., a social worker and feminist of German Jewish descent, was one of the first patients to receive psychoanalytic treatment.

Her real name was Bertha Pappenheim and she inspired much of Sigmund Freud’s works and books on psychoanalytic theory, although they hadn’t met in person. Their connection was through Joseph Breuer, Freud’s mentor when he was still starting his clinical practice.

Anna O. suffered from paralysis, personality changes, hallucinations, and rambling speech, but her doctors could not find the cause. Joseph Breuer was then called to her house for intervention and he performed psychoanalysis, also called the “talking cure”, on her.

Breuer would tell Anna O. to say anything that came to her mind, such as her thoughts, feelings, and childhood experiences. It was noted that her symptoms subsided by talking things out.

However, Breuer later referred Anna O. to the Bellevue Sanatorium, where she recovered and set out to be a renowned writer and advocate of women and children.

12. Patient HM

H.M., or Henry Gustav Molaison, was a severe amnesiac who had been the subject of countless psychological and neurological studies.

Henry was 27 when he underwent brain surgery to cure the epilepsy that he had been experiencing since childhood. In an unfortunate turn of events, he lost his memory because of the surgery and his brain also became unable to store long-term memories.

He was then regarded as someone living solely in the present, forgetting an experience as soon as it happened and only remembering bits and pieces of his past. Over the years, his amnesia and the structure of his brain had helped neuropsychologists learn more about cognitive functions .

Suzanne Corkin, a researcher, writer, and good friend of H.M., recently published a book about his life. Entitled Permanent Present Tense , this book is both a memoir and a case study following the struggles and joys of Henry Gustav Molaison.

13. Chris Sizemore

Chris Sizemore gained celebrity status in the psychology community when she was diagnosed with multiple personality disorder, now known as dissociative identity disorder.

Sizemore has several alter egos, which included Eve Black, Eve White, and Jane. Various papers about her stated that these alter egos were formed as a coping mechanism against the traumatic experiences she underwent in her childhood.

Sizemore said that although she has succeeded in unifying her alter egos into one dominant personality, there were periods in the past experienced by only one of her alter egos. For example, her husband married her Eve White alter ego and not her.

Her story inspired her psychiatrists to write a book about her, entitled The Three Faces of Eve , which was then turned into a 1957 movie of the same title.

14. David Reimer

When David was just 8 months old, he lost his penis because of a botched circumcision operation.

Psychologist John Money then advised Reimer’s parents to raise him as a girl instead, naming him Brenda. His gender reassignment was supported by subsequent surgery and hormonal therapy.

Money described Reimer’s gender reassignment as a success, but problems started to arise as Reimer was growing up. His boyishness was not completely subdued by the hormonal therapy. When he was 14 years old, he learned about the secrets of his past and he underwent gender reassignment to become male again.

Reimer became an advocate for children undergoing the same difficult situation he had been. His life story ended when he was 38 as he took his own life.

15. Kim Peek

Kim Peek was the inspiration behind Rain Man , an Oscar-winning movie about an autistic savant character played by Dustin Hoffman.

The movie was released in 1988, a time when autism wasn’t widely known and acknowledged yet. So it was an eye-opener for many people who watched the film.

In reality, Kim Peek was a non-autistic savant. He was exceptionally intelligent despite the brain abnormalities he was born with. He was like a walking encyclopedia, knowledgeable about travel routes, US zip codes, historical facts, and classical music. He also read and memorized approximately 12,000 books in his lifetime.

This list of experiments and case studies in psychology is just the tip of the iceberg! There are still countless interesting psychology studies that you can explore if you want to learn more about human behavior and dynamics.

You can also conduct your own mini-experiment or participate in a study conducted in your school or neighborhood. Just remember that there are ethical standards to follow so as not to repeat the lasting physical and emotional harm done to Little Albert or the Stanford Prison Experiment participants.

Asch, S. E. (1956). Studies of independence and conformity: I. A minority of one against a unanimous majority. Psychological Monographs: General and Applied, 70 (9), 1–70. https://doi.org/10.1037/h0093718

Bandura, A., Ross, D., & Ross, S. A. (1961). Transmission of aggression through imitation of aggressive models. The Journal of Abnormal and Social Psychology, 63 (3), 575–582. https://doi.org/10.1037/h0045925

Elliott, J., Yale University., WGBH (Television station : Boston, Mass.), & PBS DVD (Firm). (2003). A class divided. New Haven, Conn.: Yale University Films.

Festinger, L., & Carlsmith, J. M. (1959). Cognitive consequences of forced compliance. The Journal of Abnormal and Social Psychology, 58 (2), 203–210. https://doi.org/10.1037/h0041593

Haney, C., Banks, W. C., & Zimbardo, P. G. (1973). A study of prisoners and guards in a simulated prison. Naval Research Review , 30 , 4-17.

Latane, B., & Darley, J. M. (1968). Group inhibition of bystander intervention in emergencies. Journal of Personality and Social Psychology, 10 (3), 215–221. https://doi.org/10.1037/h0026570

Mischel, W. (2014). The Marshmallow Test: Mastering self-control. Little, Brown and Co.

Thorndike, E. (1920) A Constant Error in Psychological Ratings. Journal of Applied Psychology , 4 , 25-29. http://dx.doi.org/10.1037/h0071663

Watson, J. B., & Rayner, R. (1920). Conditioned emotional reactions. Journal of experimental psychology , 3 (1), 1.

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The Robbers Cave Experiment: The Psychological Study Of Unsupervised Boys That Inspired Lord Of The Flies

In an effort to test one of his theories on social behavior, psychologist muzafer sherif released 22 twelve-year-old boys into a sparsely supervised wilderness camp — and then covertly provoked them to fight each other..

The British Psychological Society /University of Akron Some of 22 12-year-old boys unknowingly en route to participate in Sherif’s Robbers Cave experiment.

In the summer of 1954, world-renowned social psychologist Muzafer Sherif toted 22 boys to the foothills of the San Bois Mountains of southeastern Oklahoma. There, in Robbers Cave State Park, he intended to conduct an unprecedented social experiment that involved pitting sparsely supervised 12-year-old boys against each other in the Oklahoma wilderness.

This was the Robbers Cave experiment, and its startling outcome would inspire the harrowing book Lord of the Flies just a year later. Nearly six decades since, experts dub the experiment unethical as it appears to have left lasting mental damage on its subjects.

The First Experiment: Camp Middle Grove

Muzafer Sherif was born in the Ottoman Empire and won a slot to study psychology at Harvard. He quickly realized that lab research on rats was too confining and he wanted a more complex subject: humans.

Fascination with social psychology had, with reason, reached a peak following WWII, and so Sherif was able to secure a grant from the Rockefeller Foundation. 

His initial experiment required that 11-year-old boys be sent under the guise of a summer camp to Middle Grove park in upstate New York. There Sherif would split the boys into teams, pit them against each other for prizes, and then try to reunite them using a series of frustrating and life-threatening events — like a forest fire. Neither the parents nor the boys, obviously, knew this was a study.

The Robbers Cave experiment, then, was the second of Sherif’s, as his study at Middle Grove in the summer of 1953 had in his mind not accomplished the outcome he had hoped for. He was looking for confirmation of his “ Realistic Conflict Theory “, which stated that groups would compete for limited resources even against their friends and allies, but come together in the face of a common disaster regardless of those alliances.

The boys at Middle Grove had not cooperated with this theory. They stayed friends despite all hardships, even when Sherif had his staffers steal their clothes, raze their tents, and smash their toys all the while framing other campers.

The experiment ended in a drunken brawl between one of the leading social psychologists in the world, Muzafer Sherif, and his research assistants as his experiment had not cooperated with him.

Sherif resolved to try again with the Robbers Cave experiment.

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The Robbers Cave Experimental Camp

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Sherif still had money from the grant for the first study but after his failure, felt that his reputation was at risk. This time he would keep the boys separated from the beginning so that they couldn’t form the pesky friendships which had thwarted the study at Middle Grove. The groups were the Rattlers and the Eagles.

The two groups were unaware of each other for the first two days. They bonded with their own group through standard camp activities like hiking and swimming.

Once the groups seemed to be solidly formed, Sherif and his team instituted the ‘competition phase’ of the Robbers Cave experiment. The groups were introduced to each other and a series of rivalrous activities were scheduled. There would be a tug-of-war, baseball and so forth. Prizes would also be awarded, trophies at stake, and there would be no consolation prizes for the losers. The Rattlers declared they would be the winners and monopolized the baseball field in order to practice.

They put their flag up on the field and told the Eagles they had better not touch it.

The Conflict

Competition is apparent on this haughty flag.

The staffers began to interfere more aggressively in the Robbers Cave experiment. They deliberately caused conflict and once arranged for one group to be late for lunch so that the other group would eat all the food.

At first, the conflict between the boys was verbal with just taunts and name-calling. But under the careful guidance of Sherif and his staff, it soon became physical. The Eagles were supplied with matches and they burned their rival’s flag. The Rattlers retaliated, invaded the Eagles’ cabin, and wrecked it and stole their belongings.

The conflict escalated to violence so that the groups had to be separated for two days.

Now that the kids hated each other, Sherif decided it was time to vindicate his theory and bring them back together. So he shut off the drinking water.

The Rattlers and Eagles set off to find the water tank which was on a mountain. The only water they had was what was in their canteens. When they arrived at the tank, hot and thirsty, the groups had already begun to merge.

Resolution and Legacy Of The Robbers Cave Experiment

The campers found the valve to the tank but it was covered with rocks, so they joined together and removed the rocks as quickly as possible. This pleased Sherif immensely as it was in direct agreement with his theory: the groups would fight over limited resources but band together when faced with a common threat.

Nevermind that the experiment was ethically and procedurally dubious, as Sherif had gotten the results that he wanted and his theory, along with the study itself, garnered great publicity. But even professionals who used the study in their textbooks doubted its value.

Six decades of development in the field have led modern psychologists to criticize the study. Sherif conducted his experiment under the belief that it was meant to showcase his theory, not either prove or disprove it. In this way, he could very easily and in many ways did, finagle the outcome he desired.

Further, the boys were all middle-class and white, and all shared a Protestant, two-parent background. The study in this way was not reflective of real-life and was considered limited. There was also the ethical issue surrounding the participants’ deception: neither the children nor their parents knew what they had consented to, and the boys were in many cases left unattended or in danger of harm.

Regardless of these qualms, the Robbers Cave experiment has left a legacy — particularly on the participants.

Now-grown camper Doug Griset recalls ironically: “I’m not traumatized by the experiment, but I don’t like lakes, camps, cabins or tents.”

If you enjoyed this article about the Robbers Cave experiment, then read about how the Stanford Prison Experiment ended in disaster or cringe at this list of the most evil scientific experiments ever performed.

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8 Fun Science Experiments You Can Easily Do at Home

Looking for a science project to do with kids? These experiments go beyond the trivial and incorporate real-world scientific research.

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Around the world, millions of kids are headed back to school in a totally different way. Classes are online. Teachers talk to students in virtual classrooms. And parents are often left looking for new, hands-on science learning opportunities.

We’ve got your back. Here are eight fun and easy science experiments that you can do at home with kids of all ages. What’s more, each of these science projects ties into real-life research efforts through citizen science, where volunteers help experts collect and analyze data.

RELATED: VIRTUAL DISSECTION: ANIMALEARNING FROM HOME

Make Wild Sourdough

It seems like the whole world is baking homemade sourdough bread right now. Sourdough took on broad appeal when the baker’s yeast disappeared from store shelves. Unlike other baking projects, sourdough doesn’t need store bought yeast. Instead, it’s made with sourdough starter.

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If you have flour, you can easily experiment with making your own sourdough starter.  Wild sourdough starters  tap into the abundant yeast in our homes and puts them to work making delicious bread. When it comes to science experiments you can do at home, few could be more delicious and rewarding than this one. You’ll also be helping scientists out along the way.

RELATED: BACK TO SCHOOL WITH CITIZEN SCIENCE

The Wild Sourdough Project is a global science experiment that hopes to discover how sourdough starter communities form over time. The team behind the effort is hoping to unravel how factors like geography and different kinds of flour affect the yeast communities. Best of all, the effort has a step-by-step guide that lets you learn how to make your own sourdough starter.

Take Part: Make Your Own Sourdough for Science

Create a Cloud in a Jar

Clouds are an important and often overlooked driver of Earth’s temperature. They trap sunlight in, but they also reflect it back into space. That role has climate scientists rushing to study our planet’s clouds, and how they’re changing.  NASA’s GLOBE Observer: Clouds  project taps citizen scientists to provide pictures of the sky, plus observations of cloud cover, type, sky conditions and visibility. That data helps info real science research and verify what satellites are seeing from space.

You can get involved with your kids and enrich the experience by adding lessons about clouds. For example, NASA has added a number of fun and easy ways to learn about climate science and clouds, including science experiments. One of the best related projects is to make a cloud in a jar. This simple science experiment is a powerful way to demonstrate how clouds work. You only need water, ice, a jar, and a few minutes of time.

Take Part: Join NASA’s Globe Observer Clouds

Measure Rain and Snow with CoCoRaHS

Fall is approaching fast, which means many of us will soon be at home watching rain and snow out the window. Instead of succumbing to the gloom, why not make that weather into a fun science experiment for your kids?

The CoCoRaHS weather monitoring program, or Community Collaborative Rain, Hail, and Snow Network, is a network of volunteers who measure and report on precipitation. CoCoRaHS emphasizes training and education, and they even have an interactive website rich in educational resources and even National Weather Service lesson plans you can use at home.

RELATED: Getting Creative with Remote Science Learning

As a volunteer, you’ll use the same low-cost weather gauges that meteorologists and cities use. Then, when it rains, snows or hails, you’ll submit your precipitation data to the website where you can compare it to others in real-time. That information also helps out the National Weather Service, as well as researchers, farmers, emergency managers — and curious people everywhere.

Take Part: Join the CoCoRaHS Weather Monitoring Network

Plant a Pollinator Garden

Pollinators play a vital role in Earth’s ecosystems, and yet they’re threatened by pesticides, disease, habitat loss and even climate change. That has many people searching for  ways to help save bees and other pollinators .

There are many options to chip in, but one of the most impactful things you and your kids can do at home is  plant a pollinator garden .

Not only will this serve to help struggling pollinators, it can also serve as a long-term science laboratory at home. SciStarter, the citizen-science group behind this blog post, has compiled an entire group of at-home science projects that can be done from your pollinator garden. You can watch moths, butterflies, bees, hummingbirds and more, then help scientists track their migration across the country.

Take Part: Plant a Pollinator Garden

Build a Bee Condo

If you already have a bumper garden at home, or it’s getting too cold to think about planting just yet, you can still stay indoors and help pollinators. The group behind National Pollinator Week has put together instructions for how you can build a home for native bees, called a bee condo. Unlike domesticated honey bees that live in apiaries, most native, wild bees you find in your backyard actually burrow their homes into the soil or a tree.

By building a bee condo, you can encourage bees to live nearby and also get a fun, DIY science experiment to do at home. Once it’s up, you can watch what kinds of critters take up residence there and report back on the results for science.

Take Part: Build a Bee Condo

Scan the Night Sky

Around the world, light pollution from buildings and street lamps is blocking our view of the night sky. Most people who live in cities have never seen a truly dark sky, or the Milky Way. That’s not just bad for humans, it’s also bad for the plants, animals and insects who are disrupted by light pollution.

If you have a budding astronomy-lover in the house, you can participate in a science project called  Globe at Night  that aims to create a world-wide measure of light pollution in our night sky.

For this science experiment, you can start making observations using only a smartphone. You’ll mark the sky’s darkness by how many stars you can see. And you can get a sky quality meter through the project to help record even better data.

Take Part: Measure Light Pollution in Your Community

Measure Water Quality

More than 1.5 million volunteers from across the planet are already taking part in a science experiment to track — and protect — Earth’s waterways. The citizen science effort is called the  EarthEcho Water Challenge , and it has users buy a water test kit for about $25, then start collecting basic water data.

Volunteers record things like water clarity, temperature, pH and dissolved oxygen. That data gets plugged into a large database, where it’s used for real science research and to help protect waterways.

Take Part: Join the Earth Echo Water Challenge

Study the Vitamin C in Your Juice

Back in the golden age of sailing, sailors worried that they’d get scurvy. A lack of vitamin C during long voyages can cause a host of health problems. Scurvy leaves you weak, causes skin problems and gum disease, and makes it harder to heal. Scurvy can even kill you. This isn’t just an old-timey concern, either. Future space explorers will have to worry about vitamin C as they head off to explore the solar system. And that’s the angle utilized by a fun citizen science project called  Space Scurvy .

The project asks students to use household items to test the vitamin C content of juices from their schools and homes. The necessary tools for this science experiment should be easy to come by, and the site has fun and simple directions for you to follow.

Take Part: Measure Vitamin C for the Space Scurvy Project

Note: Some of these projects are SciStarter Affiliates. You can use your SciStarter account email to join and earn credit for your participation in your SciStarter dashboard.

Citizen Science Lessons During the Pandemic

About the Author

Eric Betz is a science and tech writer for Discover Magazine, Astronomy Magazine, and others. He is a lover of #darkskies and pale blue dots.

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Three economists win Nobel for their research on how real life events impact society

Scott Horsley

Displayed is a file photo of a Nobel Prize medal on Dec. 8, 2020. The Nobel Prize in economic sciences was awarded to three U.S-based professors for their pioneering work with "natural experiments." Jacquelyn Martin/AP hide caption

Displayed is a file photo of a Nobel Prize medal on Dec. 8, 2020. The Nobel Prize in economic sciences was awarded to three U.S-based professors for their pioneering work with "natural experiments."

Three U.S.-based economists will share this year's Nobel Memorial Prize in Economic Sciences for their innovative work with "natural experiments" – events or policy changes in real life that allow researchers to analyze their impact on society.

David Card of the University of California at Berkeley will receive half the prize, worth 10 million Swedish kronor, or about $1.1 million, the Royal Swedish Academy of Sciences said on Monday . Joshua Angrist of the Masschusetts Institute of Technology and Guido Imbens of Stanford University will share the other half.

The Nobel Prize in literature goes to a Black writer for the first time since 1993

Controlled experiments are common in science and medicine: they allow, for example, to test new drugs by carefully selecting participants and controlling vital aspects to ensure objectivity.

But they are harder in social sciences where it can often be impractical or unethical to conduct randomized trials – unless a real-life event or policy change happens that allow researchers to conduct what are called "natural experiments."

"Natural experiments are everywhere," said Eva Mork, a member of the prize committee. "Thanks to the contributions of the laureates, we researchers are today able to answer key questions for economic and social policy. And thereby the laureates work has greatly benefited society at large."

The Nobel Economics Prize committee members announce the winners of Nobel Memorial Prize in Economic Sciences on Monday. David Card, Joshua Angrist and Guido Imbens were given the award for their research of real-life events and policy changes. Claudio Bresciani/TT News Agency/AFP via Getty Images hide caption

The Nobel Economics Prize committee members announce the winners of Nobel Memorial Prize in Economic Sciences on Monday. David Card, Joshua Angrist and Guido Imbens were given the award for their research of real-life events and policy changes.

The impact of the minimum wage

Card was recognized in part for his groundbreaking work in the early 1990s with the late Princeton economist Alan Krueger, which challenged conventional wisdom about minimum wages.

Economists had long assumed that there was a tradeoff between higher wages and jobs. If the minimum wage went up, it was thought, some workers would get higher pay but others would be laid off.

But when Card and Krueger looked at the actual effect of higher wages on fast food workers , they found no significant drop in employment.

They reached this conclusion by comparing fast food restaurants in New Jersey, which raised its minimum wage, with restaurants in neighboring Pennsylvania, which did not.

A McDonald's sign is shown on July 28 in Houston, Texas. One of the winners of the Nobel Prize in economics on Monday was cited for his work in studying the fast food industry to help determine how minimum wages impact employment. Brandon Bell/Getty Images hide caption

A McDonald's sign is shown on July 28 in Houston, Texas. One of the winners of the Nobel Prize in economics on Monday was cited for his work in studying the fast food industry to help determine how minimum wages impact employment.

Studying cause-and-effect in real life

Meanwhile, Angrist and Imbens were recognized for methodological research that helps tease out cause and effect from these accidental case studies.

During the pandemic, natural experiments have allowed researchers to study the effects of mask mandates, social distancing policies, and supplemental unemployment benefits.

The Nobel Peace Prize goes to journalists in the Philippines and Russia

Imbens said he was "stunned" to get the congratulatory wake-up call at about 2 a.m. in California.

"I was absolutely thrilled to hear the news," Imbens told reporters. "In particular hearing that I got to share this with Josh Angrist and David Card, who are both very good friends of mine."

He noted that Angrist was best man at his wedding.

Imbens said he had no idea how he would spend his share of the prize money.

• minimum wage
• Nobel Prize in Economics
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• Guido Imbens