Science, technology, engineering, and mathematics (STEM) are essential to the economy and to sustainable long-term economic growth. The demand for stem workers in the United States across industries is expected to grow faster than other occupations in the next decade.
- Response bias: Tendency for people to over- or under-state the truth
- Non-response: People who complete surveys are systematically different from those who fail to respond due to accessibility or pride
- Representative sample: One where all sources of bias have been removed
- Questionnaire wording or interviewer effects
- Recall bias: Tendency for one group to remember prior exposure in retrospective studies
- Arithmetic mean: Evenly distributes the total among individuals; can be unrepresentative when measurements are highly skewed right (i.e. per capita income)
- Median: Value dividing distribution into two equal parts; 50th percentile
- Mode: Most frequently observed outcome; rarely reported with numeric data
Little Figures Not Present
- Small samples: Estimators with large standard errors can provide seemingly very strong effects
- Low incidence rates: Need very large samples for meaningful estimates of low frequency events
- Significance levels / margins of error: Measures of the strength and precision of interference
- Ranges: Report ranges or standard deviations along with means (i.e. “normal” ranges)
- Inferring among individuals versus populations
- Clearly label chart axes
Much Ado About Nothing
- Probable error: Estimation error with probability 0.5; if estimator is approximately normal, then the PE is approximately 0.675 standard errors
- Margin of error: Estimation error with probability 0.95; if estimator is approximately normal, then the PE is approximately two standard errors
- Clinical (practical) significance: In very large samples, an effect may be significant statistically, but not in a practical sense; report confidence intervals as well as p-values
- Choice of ranges on graphs can have a huge impact on interpretation (i.e. percent change)
- Choice of proportion of y-axis to x-axis can distort as well (Note: very easy to do with modern software)
- Can also distort bar charts by having them start at positive values and/or trimming below an artificial baseline to zero
- Bar charts and pictorial graphs should have areas proportional to values
- Only make comparisons in 1-D
- Target population: Group we want to make inference regarding
- Study population: Group or items that experiment or survey is conducted on
- When comparative studies are conducted among products, treatments or groups, what is the comparison product, treatment or group?
- Control for all other potential risk factors when studying effects of factors
- Correlation does not imply causation
- Elements of causal relationships
- Association between Y and X
- Clear time ordering (X precedes Y)
- Removal of alternative explanations (controlling for other factors)
- Dose-response (when possible)
Technology has been advancing at an incredible speed for the last couple of decades, and it shows no signs of slowing down anytime soon. Modern technology now is able to “predict” certain events through numerical data and analysis. One example of this is weather forecasting. GeoSpark Analytics is a relatively young company that provides their clients a different kind of forecast: threat assessment.
AWS IQ’s large talent pool, relationship with customers, comprehensive set of services, and global marketplace are what make them a necessary ingredient for business start-ups and growth.
Amazon has greatly expanded their presence in the cloud computing space with their latest service – AWS IQ. In September 2019, AWS IQ was introduced as a service that allows enables seamless connection, interaction, and billing between businesses and cloud-computing experts. Since then, it has not only proven to be a valuable resource for businesses of various sizes and in various stages, but also a valuable asset in Amazon’s portfolio.
Two years ago, my Physics I professor mentioned the movie “Gravity” when talking about torque, velocity, and other basic physics concepts… because the movie’s writers got them all wrong. He warned his students that the way Hollywood depicts physics will make it more difficult for us to understand real-world physics.
In a way, he’s right. We watch movies and TV shows “believing” (or getting used to the idea) that huge explosions can result from minor collisions, people can jump across huge gaps, people can stand up after getting hit by a heavy object… Yeah, in the back of our minds we know that cinematic effects are added to scenes to make on-screen stories seem more interesting than everyday life. But some subtle unrealistic depictions in movies and TV shows can seem realistic… so realistic that we choose to believe them without question.
On the other hand, sci-fi films and shows like “Star Trek” have inspired future STEMists. A lot of techies and “Star Trek” fans love to talk about how the show introduced the idea of the cellphone, but there are, of course, other inventive concepts that the show introduced such as the tablet.
Remember when the iPhone was revealed back in 2007? There were rumors about how Apple will compete with cellphone companies, but no one expected Apple to introduce a “button-less cellphone”.
The iPhone gained popularity SLOWLY. Today, a lot of people (especially young adults, teenagers, and pre-teens) are obsessed with using touchscreen smartphones for gaming, messaging, camera, news, shopping, and social media apps. But back then, can you believe that few people realized how valuable the iPhone’s multi-functional capabilities are, let alone how a “button-less phone” is possible?
So, should movies and TV shows be more scientifically accurate?
In some ways, yes. Movies that try to tell more believable stories like “Gravity” should get their scripts reviewed by scientists. Fun fact: Neil deGrasse Tyson (the iconic guy behind the “We got a badass over here” meme) complained about how the stars in the night skies shown in “Titanic” were inaccurate, and James Cameron updated the scenes after hearing Tyson’s complaint because he loves science.
In other ways, sci-fi should still be promoted in the entertainment industry.
One of my favorite sci-fi scenes of all time is from the movie “Blade Runner”, starring Harrison Ford. That movie’s villain, Roy Batty, is an AI robot who said one of the most emotional movie quotes of all time – his “Tears In Rain” monologue:
I’ve seen things you people wouldn’t believe. Attack ships on fire off the shoulder of Orion. I watched C-beams glitter in the dark near the Tannhäuser Gate. All those moments will be lost in time, like tears in rain. Time to die.
You don’t really have to understand what that movie’s about. Those two things I mentioned about the villain can make some viewers think about a future of “Astro Boys”, robot ethics, innovative things, … etc.
A lot of things that seem unrealistic in Hollywood films now may end up becoming real in the future.
What if, in the future, someone invented special listening devices to hear sounds in outer space? That’s a far-fetched thought, but I guess it’s an interesting thought.
I read Jules Verne’s “Twenty Thousand Leagues Under the Sea” when I was in elementary school, and little did I know that Jules Verne is regarded as an influential man for many inventions that characterize the 20th-century. I’ve seen Leonardo da Vinci’s odd sketches of planes and tanks when I was a kid too, and little did I know that he imagined those vehicles before someone built them.
It’s pretty evident that some people come up with creative ideas and solutions that are related or inspired by unrealistic phenomena depicted in films. So, because of that, I’m against the idea of making movies “more scientifically accurate”.
Instead, I’d like to see more STEM educators teach students about biology, technology, physics, etc. through analyzing popular films that students have most likely seen. That should keep students interested in STEM, and help them better understand abstract STEM concepts.