Posts in category: science

Poaching, habitat destruction, and climate change have decimated wild African elephant populations and pushed the animals into endangered-species territory. An international project called Operation Frozen Dumbo aims to bolster elephant populations in the wild and in the world’s zoos.

Above a herd of elephants in the Selati Game Reserve in South Africa, helicopter pilot Jana Meyer and wildlife veterinarian Hayden Cuthill scan the bush for the most worthy-looking bull elephant. Once their target is selected, Ms. Meyer deftly maneuvers her aircraft to separate the animal from the herd. Dr. Cuthill quickly estimates the male’s weight and prepares the tranquilizer dose. He fixes the ampul atop a special dart, which he shoots directly into the male’s left flank.

A team on the ground will collect the elephant’s sperm as part of the Frozen Dumbo project. The samples collected in the wild are cryogenically frozen and shipped to a consortium of European zoos, which use them to artificially inseminate their females. 

Thomas Hildebrandt of the Leibniz Institute for Zoo and Wildlife Research, who has overseen the successful artificial insemination of more than 50 female elephants, is passionate about his goal: “We have to stop populating the European zoos with animals from the wild.”

Above a herd of elephants in the Selati Game Reserve in South Africa, helicopter pilot Jana Meyer and wildlife veterinarian Hayden Cuthill scan the bush for the most worthy-looking bull elephant.

Once their target is selected, Ms. Meyer deftly maneuvers her aircraft to separate the animal from the herd. Dr. Cuthill quickly estimates the male’s weight and prepares the tranquilizer dose. He fixes the ampul atop a special dart, which he shoots directly into the male’s left flank.

A team on the ground will collect the elephant’s sperm as part of an ongoing international effort to improve elephant fertility, a project called Operation Frozen Dumbo. The samples collected in the wild are cryogenically frozen and shipped to a consortium of European zoos, which use them to artificially inseminate their females.

This avoids the importation of wild elephants.

Factors such as poaching, habitat destruction, and climate change have decimated wild African elephant populations and pushed the animals into endangered-species territory. The Frozen Dumbo project aims to bolster elephant populations in the wild and in the world’s zoos.

Back at Selati, the sedated male – dubbed The Sperminator – issues a series of loud grunts. A volunteer keeps the tip of his trunk dilated with a wooden stick to ensure the animal’s breathing.

A new study from GNS Science marks a significant step in the assessment of environmental contamination in Aotearoa New Zealand’s capital.

The Urban Geochemical Atlas of Wellington provides the first baseline of the concentrations of elements in the near-surface soils of the Wellington region, highlighting where human activities have increased levels of heavy metals and other elements.

Researchers from GNS Science have measured the concentrations of 65 different elements in the soils across the Wellington region, excluding most of the Wairarapa and Kāpiti Coast.

Soil samples were taken from more than 150 sites for the study, from a mixture of public and privately-owned land. The results are presented in a series of maps. They can be used to help identify potential localized health and environmental impacts of contaminants, and to target mitigation and remediation efforts.

Several of the elements included in the atlas are metals that, in certain concentrations, can be hazardous to human health, including arsenic, cadmium, chromium, copper, mercury, nickel, lead, and zinc.

The occurrence of these metals in the soils across the country typically varies due to differences in underlying rock types, environmental conditions and human influence. However, Regine Morgenstern, GNS Science geologist, says that as the geology in the study region is relatively uniform, it’s not a significant factor influencing the elevated levels of these heavy metals found in some Wellington soils.

“The data reveals human influences on soil chemistry across the area, and this can also be clearly seen in the maps. For example, the map for lead shows the highest concentrations across much of the more densely developed areas, particularly around Wellington Harbor and the Hutt Valley, where a lot of old housing stock exists.”

Morgenstern says that it is not unexpected to find elevated levels of heavy metals in highly populated areas.

“This is common in cities worldwide, with possible sources including pollution from industrial processes, leaded petrol from our past, disposal of fossil fuel residues and household waste, and the deterioration and removal of lead-containing paints from older houses.”

The Ministry for the Environment (MfE) issues New Zealand’s soil contaminant standards for health. The standards set out the acceptable concentrations of heavy metals in soil, categorized by land use, with the most restrictive category assigned for rural residential or lifestyle blocks.

The study found some sample sites that exceeded the most restrictive thresholds for arsenic, lead and cadmium, with several samples exceeding standards set for high-density residential land use. Median lead values in Wellington soil are significantly higher than those found in New Zealand soils in general, but are below median values observed in urban soils
in Dunedin City.

The sample spacings and interpolation technique used to construct the maps provides relatively low spatial resolution, but offers a good guide for where more detailed study at higher spatial resolution may be warranted.

Morgenstern said that although residents may be concerned if they learn their home is located within an area shown in the atlas to have elevated heavy metals, the maps cannot be used to pinpoint element concentrations for individual properties.

“We know that even within one property, concentrations can vary. Lead levels might be higher closer to a house where there has been an accumulation from leaded paint, and then lower in the vegetable garden. The estimated element concentrations are indicative only and how representative the estimates are for any site can only be confirmed with further
sampling and analysis.”

Anyone interested in finding out more about the levels of heavy metals in their backyard can send a sample to the free soil-testing service Soilsafe Aotearoa. GNS Science helped to establish this service, which is run by the University of Auckland.

The atlas is the latest publication in GNS Science’s ongoing work mapping geochemical soil variation across Aotearoa New Zealand. It follows the recent release of a national atlas, and other localized studies including Dunedin and Auckland cities, Southland, Nelson-Marlborough and Otago.

GNS Science Environment and Climate Theme Leader, Giuseppe Cortese says these studies are an important addition to the wider work that GNS Science undertakes to quantify the contaminants present in our air, freshwater, ocean and soil, and to understand how these contaminants move through our environment.

“It can be confronting to see the impact that human settlement and urbanization has had on Wellington’s soils, and the heavy metals that have accumulated as a result of our activities. The atlas provides important information about the location and concentration of key elements so that we can monitor them, apply remediation measures if necessary, and manage their movement today.”

“The data also offers a starting point from which we can explore and model how these elements may move through our environment in the future. This is particularly important in the Wellington area where projected population increases will result in further urban densification,” says Cortese.

For the first time since 1972 a spacecraft launched from the U.S. has landed softly on the surface of the moon. And, for the first time ever, this successful extraterrestrial landing was achieved by a spacecraft built and operated by private industry rather than by a government space program.

At 6:23 P.M. EST a 14.1-foot-tall lander resembling a police booth on stilts descended to the moon’s surface on a ballooning blue flame of rocket exhaust. Seconds later, the lander’s six feet crunched into the dark soil of Malapert A, a crater nestled deep in the moon’s southern latitudes.

This robotic voyager, aptly nicknamed Odysseus, carries six scientific payloads on behalf of NASA. But crucially, the U.S. space agency isn’t running the mission: Odysseus is the first commercial spacecraft ever to land safely on another celestial body.

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Odysseus was built and operated by Intuitive Machines, a private spaceflight company based in Houston, as part of the company’s IM-1 mission. In addition to NASA equipment, Odysseus carries payloads from private clients that range from a group of sculptures by artist Jeff Koons to a robotic “selfie” camera built by students at Embry-Riddle Aeronautical University.

And like its namesake from the ancient Greek epics, Odysseus faced trials as it sailed toward the lunar surface. Mere hours before landing, two onboard ranging lasers that Odysseus had planned to use to detect the moon’s surface broke. In response, Intuitive Machines improvised a software patch that let Odysseus commandeer two lasers onboard an experimental navigation payload built by NASA. 

For more than 15 minutes after touchdown, Intuitive Machines’ mission control in Houston, Texas, waited in tense silence, as flight controllers attempted to establish contact with Odysseus. “Signs of life—we have a return signal we’re tracking,” quipped Tim Crain, Intuitive Machines’ chief technology officer IM-1 mission director. “We’re also not dead yet.”

Minutes later, Crain confirmed that Odysseus was transmitting from the moon’s surface, albeit weakly. At press, the reason for the signal’s weakness remains unclear.

IM-1 is the first U.S. mission to touch down softly on the lunar surface since Apollo 17 in 1972. And unlike IM-1, Apollo 17 was crewed. The nation’s last robotic soft landing on the moon took place in January 1968, with the touchdown of the NASA lander Surveyor 7.

“Odysseus has taken the moon,” NASA administrator Bill Nelson said in a pre-recorded congratulatory message. “This feat is a giant leap forward for all of humanity.”

The mission also achieves some technical firsts. The spacecraft’s main engine—which burns liquid methane and liquid oxygen—is the first of its kind to be used in a moon landing. IM-1 also marks the southernmost moon landing ever completed. The lunar lander of India’s Chandrayaan-3 mission, the first in this general region, touched down at 69 degrees south latitude, which on Earth would be like landing on the Antarctic Peninsula. IM-1, however, is sitting at more than 80 degrees south latitude—the lunar equivalent of the deep Antarctic interior. 

IM-1’s onboard NASA instruments will provide the first in situ measurements of this forbidding environment, where the sun’s extreme angle on the horizon can create huge swings in surface temperatures, as well as in exposure to the “solar wind” of charged particles that are continuously belched out by our star. These data will include crucial radio measurements that will capture some of the solar wind’s interactions with the moon’s surface.

NASA is targeting the lunar south pole because some shadow-cloaked regions there contain water ice—a key resource for long-term human sojourns on the moon. For the agency’s Artemis III mission, which will launch no sooner than 2026, NASA has contracted with SpaceX to land a two-person crew near the lunar south pole.

“[IM-1] is a tech demo, if you like, but it will get our first data about the environment of the south pole of the moon. That’s going to be critical for designing systems to allow humans to survive and thrive there,” says University of Notre Dame lunar scientist Clive Neal.

Perhaps IM-1’s biggest contribution is the precedent it sets for the future of space exploration. For decades, space had been considered the purview of only a handful of government agencies. But thanks to plummeting launch costs and the steady march of technological progress, it’s now cheaper than ever for countries and private companies to build and operate spacecraft—and even send them to interplanetary destinations. 

“[IM-1 is] a watershed in commercial development within the United States,” Neal says.

High Risk, High Reward

At 1:05 A.M. EST on February 15 IM-1 launched atop one of SpaceX’s Falcon 9 rockets from NASA’s Kennedy Space Center in Florida. Over the next several days, Odysseus traveled a total of more than one million kilometers (621,000 miles) to insert itself into lunar orbit, which it successfully did on February 21. The spacecraft is expected to operate on the moon’s surface for up to seven days before it succumbs to the darkness and brutal cold of lunar night.

The mission is flying under the banner of NASA’s Commercial Lunar Payload Services (CLPS) initiative, which has encouraged private investment in lunar missions since its founding in 2018. Under CLPS, the agency awards private companies contracts to deliver NASA equipment and science instruments to the moon’s surface. So far 14 companies have joined the program, which promises to pay up to $2.6 billion for delivery services through 2028.

Unlike traditional NASA programs, the space agency doesn’t own and operate CLPS spacecraft—the companies do. In return, NASA hopes to achieve lower costs and a higher cadence of missions. To date, NASA has paid Intuitive Machines $118 million under the contract that created IM-1—far less than the agency has spent on robotic landers in the past. And IM-1 is the second of up to five CLPS missions that may end up launching this year.

That said, CLPS companies have been given a steep hill to climb. Historically, only five out of every nine moon missions have succeeded, even among those of well-funded government space agencies. In August 2023 the Russian moon mission Luna-25 crashed into the lunar surface after an engine misfire. In January a Japanese lunar lander known as SLIM (Smart Lander for Investigating Moon) touched down safely but at an unexpected angle, which limited its ability to collect solar power.

And in exchange for lower costs and more missions, NASA took on a higher risk that any one CLPS mission would fail. From CLPS’s inception, NASA officials cautioned that even a 50 percent mission success rate was acceptable for the program. 

So far that prediction is panning out. Back in January the Pittsburgh-based company Astrobotic attempted the first mission under CLPS, Peregrine Mission 1. Soon after launch, however, Astrobotic’s Peregrine spacecraft sprang a propellant leak. The company managed to keep the lander alive in space for a week and a half, but the mission ended with Peregrine burning up in Earth’s atmosphere.

“[NASA] expected an approximately 50 percent failure rate, and one for two is that rate,” says Laura Forczyk, executive director of the space industry consulting firm Astralytical. “[IM-1 proves] that there is a capability for commercial landers to safely land on the surface of the moon at a lower cost.”

Peregrine and IM-1 are just the first in an upcoming wave of commercial moon missions with increasingly ambitious goals. As soon as later this year, Astrobotic is on tap to deliver VIPER (Volatiles Investigating Polar Exploration Rover), a water-hunting rover built by NASA, to the lunar south pole. Intuitive Machines’ upcoming IM-2 mission, also slated for later this year, will deliver PRIME-1 (Polar Resources Ice Mining Experiment 1), a NASA drill designed to dig into the moon’s subsurface. 

“These initial missions are more test missions,” Forczyk says. “We want to make sure that the technology is proven and mature before we put higher-stakes payloads onboard.”

If great apes are known for anything, it’s being great in size. A large gorilla can weigh nearly 500 pounds, for example, and the extinct great ape Gigantopithecus was likely a hundred pounds or so heavier, the largest primate of all time. But now paleontologists have identified what might be the smallest of the great apes, a primate that lived in the forests of prehistoric Germany and was the size of a small dog.

Scientists have uncovered the fossils of two teeth and a kneecap that they say represent a new primate, named Buronius manfredschmidi. Paltry as that may seem, the teeth of fossilized mammals are often very distinctive and allow researchers to set species apart from each other. In this case, University of Tübingen paleoanthropologist Madelaine Böhme and colleagues propose, Buronius was a previously unknown species of hominid, or great ape, that lived in warm forests about 11.6 million years ago. The researchers described the fossils on Friday in PLOS One.

The first fossils of the ancient ape were uncovered more than a decade ago. In 2011, Böhme recalls, researchers began searching for fossils at a site called Hammerschmiede in southern Germany. “In the very first year,” she says, “we discovered the upper molar and the kneecap of Buronius right next to each other.”

Böhme and co-authors were confident the initial finds were from a primate, but they didn’t immediately know what sort. “Due to their small size, we initially thought they were pliopithecines,” Böhme says. Pliopithecines were prehistoric monkeys that spread through Eurasia during the Miocene, between 5.3 million and 23 million years ago, and some were quite small. The first tooth and the small bone could have come from such a primate, but too little had yet been uncovered for the researchers to be sure. The fossils waited for additional finds to place them in context.

The identity of Buronius didn’t come into focus until the researchers found the fossils of another ape at the same site. In 2015, Böhme and colleagues found the fossils of a hominid the researchers would eventually name Danuvius guggenmosi. Based on the available fossils, the researchers estimated that Danuvius could get to be about 68 pounds and was a much larger animal than the creature represented by the tooth and kneecap.

Studying the teeth of Danuvius led the researchers to reassess the 2011 finds, and a second mystery tooth had also been found in 2017. The fossils represented an additional ape, Buronius, that would have weighed about 22 pounds in life. “Now published, Buronius is the smallest known hominid,” Böhme says.

But other researchers aren’t entirely sure that Buronius and Danuvius are different species. The fossilized kneecap is small, but it could have come from a juvenile or a different sex of Danuvius, says Brooklyn College paleoanthropologist Kelsey Pugh, who was not involved in the new study. Both modern and extinct apes could greatly vary in size between sexes, she says, and so the small size of the kneecap is not enough to tell what primate species it belonged to.

With the kneecap posing questions, dental details are what principally distinguish the larger and smaller apes. The new study says that the size difference between the Buronius and Danuvius molars are greater than in any one species within the broader group of primates to which apes belong. The significant size disparity adds some support to the case that the molar found in 2011 and the Danuvius fossils represent different species.

In addition to the size difference between the molars, the interior details also distinguish the new tooth from that of Danuvius. The enamel of the Buronius molar is much thinner than that of a comparable Danuvius molar, hinting that the smaller ape was feeding on softer foods, like leaves, than its larger relative. “While enamel thickness is quite variable in living hominoids, the two Danuvius molars are quite distinct in this feature, reasonably indicating a difference in diet,” Pugh says.

The dietary difference hints at what vegetation grew in the Miocene habitat where the apes dwelled. Back when Buronius and Danuvius were alive, what’s now Bavaria was a flat, swampy landscape at the foot of the ancient Alps. The immediate area the fossils were preserved in hosted a meandering stream, Böhme says, surrounded by scrubby plants that grew densely along the banks. And even though the global climate was warmer than today, the area was far enough to the north that there would have been changes through the seasons, the fresh leaves of the spring and summer becoming rarer as the short autumn and winter days set in. How the apes foraged must have changed as the seasons affected how the local plants grew.

Selecting different foods would have allowed the apes of Hammerschmiede to coexist. “There are lots of examples of modern ecosystems where large numbers of primate species manage to coexist,” says University of Toronto Scarborough paleontologist Mary Silcox, who was not involved in the new study. The Kibale forest of Uganda, she notes, is home to 14 overlapping primate species, ranging from bush babies to monkeys and apes like chimpanzees. “Primates are very good at partitioning niche space,” she says, and the fossil record indicates that monkeys, apes and their relatives have been finding ways to coexist in the same forests for tens of millions of years.

Part of what allows multiple monkey and ape species to coexist today is that many live in warm equatorial forests where food is available year-round. The situation at Hammerschmiede is different, showing the resourcefulness of the apes found there. The vegetation where Buronius and Danuvius lived had deciduous trees and changed through the year. “It speaks to the productivity of the Hammerschmiede paleo-ecosystems that they allowed two great apes to coexist despite the seasonal food scarcity,” Böhme says.

The details of that coexistence await more fossils that will allow paleontologists to get a better view at what Hammerschmiede’s apes were like in life. More skeletal pieces will help sort out the relationships of the site’s apes, as well, which in turn will help paleoprimatologists understand how the prehistoric apes lived in a place quite different from the habitats of nonhuman apes today. Despite differences in interpretation, Pugh says, “it takes a lot of work to discover new fossils, and these authors have done an incredible job discovering new ape fossils at Hammerschmiede.” With luck, even more fossils will soon bring these ancient relatives of ours into clearer view.

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In a recent interview for the web series Hot Ones, where guests answer tough questions while eating super-spicy chicken wings, pro basketball superstar Stephen Curry was asked about a sports psychology study from the 1980s. The subject of the study was the “hot hand effect,” the idea that players’ successes come in streaks—dependent on some mysterious inner quality that ebbs and flows—and when a player’s hand is hottest and they’re “in the zone,” it can feel almost like they can’t miss.

Curry knows a thing or two about a hot hand. He holds the NBA record for making at least one three-pointer in 268 consecutive games. Following a practice one day, he sank 105 three-pointers in a row. In the Hot Ones interview, Curry said the authors of the study “don’t know what they’re talking about at all.” When Curry totaled 60 points in one game, he said, “It’s literally a tangible, physical sensation of all I need is to get this ball off my fingertips and it’s going to go in.”

The study, “The Hot Hand in Basketball: On the Misperception of Random Sequences,” was written in 1985 by eminent psychologists Thomas Gilovich, Robert Vallone, and Amos Tversky. They allegedly demonstrated through an analysis of basketball shooting data that the hot hand was a myth. Recently deceased Nobel laureate (and Tversky’s chief collaborator) Daniel Kahneman proclaimed, “The hot hand is a massive and widespread cognitive illusion.” In statistical circles, the hot hand study has taken on a metaphorical significance beyond basketball. For decades, the fallacy of the hot hand has been cited to show that folktales of luck and streakiness are no match for cold, hard numbers.

However, the numbers do not prove the hot hand is a figment of imagination. Economists Joshua Miller of the University of Melbourne and Adam Sanjurjo at the University of Alicante in Spain, used data from multiple different sports, including the same basketball data used by Gilovich, Vallone, and Tversky, to provide robust support for a hot hand. The problem, say Miller and Sanjurjo, lies in the way the original authors analyzed their data and, in particular, a mistake they made about what random data should look like without the influence of a hot hand.

The mistake is this: Imagine we’re looking at a chart of hits and misses over some number of shots and trying to find where a shooter might have had a hot hand. Suppose we look for hot-handedness by considering only those attempts that came after a sequence of hits, such as three baskets in a row. These sequences are candidates for being shot with a “hot hand.”      

Shouldn’t years of testimony from athletes like Stephen Curry count for something?

However, if there’s no such thing as a hot hand, we might expect the player to have the same success rate in these attempts—the shots after three consecutive makes—as their overall average. Since our working theory is that the previous successes have no predictive power for the next shot, it would seem intuitive that our choice of shots based on what happened right before the hot streak shouldn’t matter at all.      

But that’s wrong. By selecting attempts that come after a hot streak and computing a proportion over this subset, we have unwittingly introduced a negative bias into the estimate of the rate of success that could counteract a hot-hand-induced positive effect. In other words, the observed percentage of success-following-success being equal to the rate of success-following-anything, would be evidence for the hot hand instead of against it. The way we selected the data artificially penalized the shooter; their true success rate may have been a few percentage points higher than what we tabulated. So, if our observation matched their usual average, it must be that something else was at work to offset our bias—a hot hand. 

If you find yourself doubting this bias exists, you’re in good company, including the esteemed professors who first “debunked” the hot hand. Like other famously counterintuitive examples in probability, such as the Monty Hall Problem—the puzzle of whether to switch doors when searching for a prize on the game show Let’s Make a Deal—the phenomenon acts almost like an optical illusion: Our natural senses tell us something that turns out to be contradicted when we try to confirm it with hard measurement.

I’ll freely admit that I didn’t believe it either, at first. I only became convinced after I ran 100,000 simulations of 100 random coin flips and tabulated the proportions of heads following streaks of three heads. I knew the coin flips were perfectly random 50/50 processes under any conditions (coins don’t get “hot”). So, if I estimated the success rate at anything less than 50 percent, the bias was real. The overall average proportion of heads-after-three-heads I got was around 46 percent. The histogram (data visualization) of results clearly showed the negative bias. Without understanding the source of the bias, we might think the coin had the opposite of a hot hand. This was mechanically the same procedure as the original 1985 study, showing the study was flawed and its conclusions likely invalidated.   

To see how this could possibly work, consider a simplified example of three coin flips. Suppose we want to determine if a coin coming up heads is more likely to be followed by another heads—to see if the coin can get “hot.” There are eight equally likely sequences of flips:

HHH

HHT

HTH

HTT

THH

THT

TTH

TTT

Out of these, the proportions of heads being followed by heads are as follows:

1

½

0

0

1

0

N/A

N/A

(We discard the last two cases because no coin flip came after a head.) Averaging the estimated proportions over the remaining six cases yields (1+½+1)/6 = 5/12, or about 42 percent. That is decidedly less than 50/50.

Even though we know the coin has no memory or “momentum,” estimating this way produces a number less than the true success probability in these situations, on average. As Miller and Sanjurjo demonstrated, this bias gets larger when you consider smaller samples and longer streaks. In the typical experiments like those in Gilovich, Vallone, and Tversky’s work, looking at streaks of two or three in samples of around 100, it could make a sizable difference.

Now, we might expect some strange things to crop up when we process the data the way I have described above. For one thing, we’re talking about an average proportion: the average over all the possible universes of the proportion of attempted shots that were successful after a streak of hits. Further, “average proportions” are notoriously finicky, especially when the proportions are being computed over different numbers of events, as they are here.

The numbers do not prove the hot hand is a figment of imagination.

In some sequences of coin flips in the table above, we had two cases to consider (such as the first and second ones) while in others there was only one. Compiling proportions taken over different sized sets is how we get famous examples of weird behavior like “Simpson’s Paradox,” in which a variable may trend one way among subsets of a larger group while the overall trend is the opposite.

For another thing, in our dataset of hits and misses, some of the streaks we might be considering necessarily overlap with each other. For example, if somewhere in the midst of 100 coin flips we had the sequence HHHHT, this would account for two streaks of three heads, one that’s followed by another heads and a second followed by tails. This means, in fact, that unless the segment we’re currently considering lasts until the very end of the data, there must at some point be a streak that ends in failure. The necessity of these failures contributes to the negative bias in our estimate of the rate of success.

Of course, real-world data is not like an idealized sequence of coin flips, and in a team sport like basketball, the picture is made messier thanks to confounding variables. This is due to an overall lack of available data from real games, and the fact that, often, when a player gets hot, defenses will change their strategy to compensate.  

What’s more, a player on a shooting streak, who may have a hot hand or not (some streaks are just luck), may take greater risks, such as attempting more difficult shots, known colloquially as “heat checks.” If these attempts result in misses, then a look at just the data would suggest the player was never hot. Some players may get nervous after a few successes because they feel all eyes are on them, whereas for others the pressure of a big game or a cheering crowd may be integral to the emergence of a hot hand. Controlled experiments where the strategic factors are held fixed but the psychological environment is preserved are hard to come by.

That’s why Miller and Sanjurjo claim perhaps the strongest evidence for the hot hand effect is in the three-point shooting contest, held during the NBA’s All-Star weekend festivities; there’s no defense and the shot locations are fixed. Players have 70 seconds to shoot up to 27 balls. Performances like Craig Hodges hitting 19 shots in a row in 1991 seem like clear examples of a hot hand at work. (Curry has won the contest twice.) From there, Miller said, it’s reasonable to extrapolate to in-game settings, and “then you can couple that with what the players themselves say.” Or do. “This guy shot the basketball and started walking the other way,” commentator Mark Jackson once remarked during a game after Dwyane Wade, the former Miami Heat star, launched a three-pointer with such confidence that he didn’t bother to watch it go in. “That’s called being in the zone.”

The saga of the hot hand effect offers at least one final cautionary tale.

However, there’s the crux of the issue: Should we trust people’s subjective experiences of what it’s like to be “in the zone,” or should we assume there’s no such thing until it shows up in the data? Cognitive biases, such as finding patterns where none exist, are certainly real, and people’s ability to discern the presence of randomness is notoriously bad. Go to any casino and watch at the roulette table as people try to spot streaks in the most recent spins or look online for advice about tradeable “signals” in the movements of stock prices. But shouldn’t years of testimony from high-performing athletes like Curry count for something?

The claim that the hot hand is an illusion is equivalent to saying players’ skill levels are perfectly constant through time. Compared to the hot hand effect, this is arguably the bolder statement in need of evidentiary support. Many human activities—from sports to literature to theoretical physics—involve some combination of skill and luck to create success. But it’s misleading to think of the “luck” component of that mix as truly “random.”

Instead, it’s perhaps better described as uncontrollable by the person involved in performing the action—the tiny fluctuations in the conditions of the ball, fingers, arms, feet—the unaccounted-for variations and moments of inspiration that are beyond the skill level of that particular player to reliably dictate at that particular moment.

But who’s to say the boundary between controllable and uncontrollable can’t shift? The relative shares of “luck” vs. “skill” could be dependent on many situational factors. Players might reliably be able to sense when finer details that are normally beyond their control are suddenly within their grasp. That’s “the zone.”

The saga of the hot hand effect offers at least one final cautionary tale about the enduring power of random phenomena to fool us all (myself included), even—maybe especially—when we think we’re unfoolable. The counterintuitive properties of streaks in random sequences tricked the original researchers into overlooking a pattern that was really there in the data. The percentage of successes under their method of selecting candidate shots included a bias that erased the effect of the hot hand to boost the success rate.

But by simulating synthetic data under known assumptions and verifying that their methods retrieved the right answers, they might have protected themselves against the illusions of probability. Tversky, after publishing his research and becoming one of the most outspoken critics of the hot hand, once said as much: “It may be that the only way you can learn about randomness is to toss coins on the side while you play.” During the NBA Finals this week between the Celtics and Mavericks, if you happen to notice any players on the bench compiling histograms of coin tosses, they might just be trying to understand whether it’s statistically valid to conclude their teammates are heating up.

Lead photo: Boston Celtics center Kristaps Porzingis shoots over Dallas Mavericks guard Luka Doncic during Game 1 of the 2024 NBA Finals. (AP Photo/Charles Krupa)

• Aubrey Clayton

  Posted on June 7, 2024

  Aubrey Clayton is an applied mathematical researcher, lecturer, and writer. He is the child of two math teachers and the author of Bernoulli’s Fallacy: Statistical Illogic and the Crisis of Modern Science.

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An estimated 100 billion stars make up the Milky Way. Our home galaxy stretches 100,000 light-years across, an astounding size that can be difficult to fathom until you sit outside on a dark night, look up, and are hit with the sight of a brushstroke of stars and space dust splashed against the sky.

Each year, the Milky Way Photographer of the Year awards honor the photographers who capture the beauty of our galaxy from down here on Earth. Capture the Atlas selected 25 winners from more than 5,000 entries submitted from around the world. The final list includes photographs shot in 19 different countries, including Australia, New Zealand, Chile, Argentina, France, Switzerland, Spain, Italy, Bulgaria, Slovenia, Egypt, Oman, Yemen, Jordan and the United States.

Photographer Tom Rae said of his photo, snapped against the backdrop of New Zealand’s highest mountain: “It embodies the dedication, sleepless nights, and the fulfillment of completing my vision. The image features icebergs in the cyan-blue glacial lake, red airglow painting the sky, and the glow of billions of stars in the Milky Way—a glimpse into the vastness beyond.”