YAY Oscars! Last night’s Academy Awards ceremony did not disappoint- political commentary mixed with some humor, beautiful dresses (and people), some maaajor drama, and even some science! Pretty sure there was more mention of science during the Oscar’s than there has been during any of Trumps speeches ever (someone fact check me on that). Here are the highlights:
Hidden Figures nominated for several Oscars: While it didn’t take home any awards, Hidden Figures was nominated for Best Picture, Best Supporting Actress (Octavia Spencer), and Best Writing Adapted Screenplay.This film is the story of a team of African-American women mathematicians who served a vital role in NASA during the early years of the US space program. This is a major shoutout to both women and minorities in science, and it’s always great when a science film makes it to the awards show. Not to mention, our favorite scientist Neil deGrasse Tyson was live tweeting about the film throughout the Oscars.
The film @HiddenFigures reminds us: Even with America’s problems, in the 1960s we all knew the importance of Science & Math.
GE wins “Best Commercial” with it’s ad geared towards hiring more women scientists: YAAAS. This was one of the best commercials I’ve ever seen. Huge kudos to GE for dedicating efforts to hiring more women in STEM and advertising this initiative in a well-done commercial. It’s beautiful.
Shoutout to Science and Tech awards: While they unfortunately have their own separate event, it’s nice that the Oscars took the time to highlight some of the innovation made from the science and technology sectors and their contribution to film making. Some of these awards went to facial-performance-capture technology, animation technology, and improvements in digital camera systems.
In light of the recent scandal where an American Airlines flight was delayed when a passenger reported a Professor’s Math Equations as “suspicious”, twitter has responded with the #PassesNoteToFlightAttendant hashtag- Funny ways in which scientists doing normal tasks could be perceived as “suspicious”. Enjoy!
Pi Day is celebrated on March 14th (3/14) around the world. Pi (Greek letter “π”) is the symbol used in mathematics to represent a constant — the ratio of the circumference of a circle to its diameter — which is approximately 3.14159.
Pi has been calculated to over one trillion digits beyond its decimal point. As an irrational and transcendental number, it will continue infinitely without repetition or pattern. While only a handful of digits are needed for typical calculations, Pi’s infinite nature makes it a fun challenge to memorize, and to computationally calculate more and more digits.
Today’s Astronomy Picture of the Day is more than a picture… actually a video!!! “How different does the universe look on small, medium, and large scales?”
Notably, Noether overcame immense adversity and pioneered the field in both math and physics (being a female jewish professor in early Nazi Germany was not exactly the most welcoming environment).
This year will be a very special Pi Day! Pi day falls on March 14th every year (representing the first 3 digits of pi: 3.14). On Saturday, 3.14.15 at exactly 9:26:53 AM & PM – the date and time will reflect the first 10 digits of the mathematical constant pi. Yippee! Looking for a fun way to celebrate? Bake a pie, ofcourse.
The ESA Rosetta Mission included at least four women who are listed as team members, but I would guess there are many more who contributed but are not listed!
It takes hundreds of people — machinists, engineers, scientists, and many others — to get a spacecraft from the planning stages to its destination in outer space. The people in this gallery represent just a few of the folks who make space exploration ideas a reality.
Let’s celebrate Claudia Alexander (U.S. Rosetta Project Scientist), Margaret Frerking (Co-I with MIRO instrument), Lori Feaga, (ALICE Co-I with University of Maryland), Marilia Samara (ScRI, EIS instrument), and the many other women who contributed to the Rosetta Mission. CauseScience applauds all of these women for their amazing success today, and over the last decade of the mission. These women are the best at what they do, and break down barriers for girls and women in Science, Technology, Engineering, and Math!! CONGRATS!!
According to the study, those whose minds were elsewhere while being taught certain concepts, like what a virus is and numbers, are at a significantly greater risk of being afraid of catching Ebola than people who were paying even scant attention.
For example, when a participant of the study was told that he had a one-in-thirteen-million chance of contracting the virus, his response was, “Whoa. Thirteen million is a really big number. That is totally scary.”
The recent confirmation of the first Ebola case in Texas confirms the studies predictions… to the day. The calculated risk of an Ebola case in the USA by the end of september was 18%.
To jog your memory of that post:
A study published in PLOS Currents: Outbreaks calculated the likelihood of Ebola cases coming to the United States and other countries based on virtual airline traffic. The study concluded that within 3-6 weeks the ‘probability of international spread outside the African region is small, but not negligible.’
… the authors of a new analysis say many countries — including the U.S. — should gear up to recognize, isolate and treat imported cases of Ebola.
The probability of seeing at least one imported case of Ebola in the U.S. is as high as 18 percent by late September…
These predictions are based on the flow of airline passengers from West Africa and the difficulty of preventing an infected passenger from boarding a flight.
UNDERSTANDING RESEARCH: What do we actually mean by research and how does it help inform our understanding of things? Today we look at the dangers of making a link between unrelated results.
Here’s an historical tidbit you may not be aware of. Between the years 1860 and 1940, as the number of Methodist ministers living in New England increased, so too did the amount of Cuban rum imported into Boston – and they both increased in an extremely similar way. Thus, Methodist ministers must have bought up lots of rum in that time period!
Actually no, that’s a silly conclusion to draw. What’s really going on is that both quantities – Methodist ministers and Cuban rum – were driven upwards by other factors, such as population growth.
Two quantities are said to be correlated if both increase and decrease together (“positively correlated”), or if one increases when the other decreases and vice-versa (“negatively correlated”).
Correlation is readily detected through statistical measurements of the Pearson’s correlation coefficient, which indicates how tightly locked together the two quantities are, ranging from -1 (perfectly negatively correlated) through 0 (not at all correlated) and up to 1 (perfectly positively correlated).
But just because two quantities are correlated does not necessarily mean that one is directly causing the other to change. Correlation does not imply causation, just like cloudy weather does not imply rainfall, even though the reverse is true.
Even where causation is present, we must be careful not to mix up the cause with the effect, or else we might conclude, for example, that an increased use of heaters causes colder weather.
In order to establish cause-and-effect, we need to go beyond the statistics and look for separate evidence (of a scientific or historical nature) and logical reasoning. Correlation may prompt us to go looking for such evidence in the first place, but it is by no means a proof in its own right.
Subtle issues
Although the above examples were obviously silly, correlation is very often mistaken for causation in ways that are not immediately obvious in the real world. When reading and interpreting statistics, one must take great care to understand exactly what the data and its statistics are implying – and more importantly, what they are not implying.
One recent example of the need for caution in interpreting data is the excitement earlier this year surrounding the apparent groundbreaking detection of gravitational waves – an announcement that appears to have been made prematurely, before all the variables that were affecting the data were accounted for.
Unfortunately, analysing statistics, probabilities and risks is not a skill set wired into our human intuition, and so is all too easy to be led astray. Entire books have been written on the subtle ways in which statistics can be misinterpreted (or used to mislead). To help keep your guard up, here are some common slippery statistical problems that you should be aware of:
1) The Healthy Worker Effect, where sometimes two groups cannot be directly compared on a level playing field.
Consider a hypothetical study comparing the health of a group of office-workers with the health of a group of astronauts. If the study shows no significant difference between the two – no correlation between healthiness and working environment – are we to conclude that living and working in space carries no long-term health risks for astronauts?
No! The groups are not on the same footing: the astronaut corps screen applicants to find healthy candidates, who then maintain a comprehensive fitness regime in order to proactively combat the effects of living in “microgravity”.
We would therefore expect them to be significant healthier than office workers, on average, and should rightly be concerned if they were not.
2) Categorisation and the Stage Migration Effect – shuffling people between groups can have dramatic effects on statistical outcomes.
This is also known as the Will Rogers effect, after the US comedian who reportedly quipped:
When the Okies left Oklahoma and moved to California, they raised the average intelligence level in both states.
To illustrate, imagine dividing a large group of friends into a “short” group and a “tall” group (perhaps in order to arrange them for a photo). Having done so, it’s surprisingly easy to raise the average height of both groups at once.
Simply ask the shortest person in the “tall” group to switch over to the “short” group. The “tall”‘ group lose their shortest member, thus bumping up their average height – but the “short” group gain their tallest member yet, and thus also gain in average height.
This has major implications in medical studies, where patients are often sorted into “healthy” or “unhealthy” groups in the course of testing a new treatment. If diagnostic methods improve, some very-slightly-unhealthy patients may be recategorised – leading to the health outcomes of both groups improving, regardless of how effective (or not) the treatment is.
Picking and choosing among the data can lead to the wrong conclusions. The skeptics see period of cooling (blue) when the data really shows long-term warming (green). skepticalscience.com
3) Data mining – when an abundance of data is present, bits and pieces can be cherry-picked to support any desired conclusion.
This is bad statistical practice, but if done deliberately can be hard to spot without knowledge of the original, complete data set.
Consider the above graph showing two interpretations of global warming data, for instance. Or fluoride – in small amounts it is one of the most effective preventative medicines in history, but the positive effect disappears entirely if one only ever considers toxic quantities of fluoride.
For similar reasons, it is important that the procedures for a given statistical experiment are fixed in place before the experiment begins and then remain unchanged until the experiment ends.
4) Clustering – which is to be expected even in completely random data.
Consider a medical study examining how a particular disease, such as cancer or Multiple sclerosis, is geographically distributed. If the disease strikes at random (and the environment has no effect) we would expect to see numerous clusters of patients as a matter of course. If patients are spread out perfectly evenly, the distribution would be most un-random indeed!
So the presence of a single cluster, or a number of small clusters of cases, is entirely normal. Sophisticated statistical methods are needed to determine just how much clustering is required to deduce that something in that area might be causing the illness.
Unfortunately, any cluster at all – even a non-significant one – makes for an easy (and at first glance, compelling) news headline.
Statistical analysis, like any other powerful tool, must be used very carefully – and in particular, one must always be careful when drawing conclusions based on the fact that two quantities are correlated.
Instead, we must always insist on separate evidence to argue for cause-and-effect – and that evidence will not come in the form of a single statistical number.
Seemingly compelling correlations, say between given genes and schizophrenia or between a high fat diet and heart disease, may turn out to be based on very dubious methodology.
The bad news is that our evolution equipped us to live in small, stable, hunter-gatherer societies. We are Pleistocene people, but our languaged brains have created massive, multicultural, technologically sophisticated and rapidly changing societies for us to live in.
In consequence, we must constantly resist the temptation to see meaning in chance and to confuse correlation and causation.
The authors do not work for, consult to, own shares in or receive funding from any company or organisation that would benefit from this article. They also have no relevant affiliations.
Photo: ESA
